最新博客文章
200AI 开发实战教程，从零到一构建你的 AI 应用。
All200
Beginner32
Intermediate92
Advanced76
All
Prompt Engineering12
RAG Retrieval Augmented17
Agent Development34
Model Fine-tuning21
Deployment and Operations25
AI Application Development40
DeepSeek API16
Multimodal Development6
AI Safety6
Model Inference3
Retrieval Systems1
RAG Retrieval1
Model Architecture3
API Development1
Agent Development10
Model Deployment1
Multimodal1
Model Evaluation2
Tutorial
Date
DeepSeek API Development Documentation
AI Application Development
Beginner
Complete DeepSeek API development documentation: authentication configuration, Chat Completions, Function Calling, streaming output, JSON mode, and token calculation. Includes Python/JavaScript/Go SDK examples and best practices to quickly get started with the DeepSeek API.
High-Performance Inference Deployment with vLLM
Deployment and Operations
Intermediate
vLLM is currently the most advanced open-source LLM inference engine. This article provides an in-depth explanation of vLLM's PagedAttention principle, production environment configuration, performance tuning strategies, and monitoring solutions to help you build high-throughput inference services.
用户记忆和知识库
RAG Retrieval Augmented
Intermediate
上一章解决的是单次交互的上下文管理。这一章要处理一个更难的问题：如何让 Agent 在对话结束后仍然记住用户、记住知识。
Dialogue Prefix Continuation: Let AI Perfectly Complete Your Half-Sentence
DeepSeek API
Advanced
DeepSeek V4's Chat Prefix Completion (Beta) feature allows the model to continue generating from a specified prefix. This article details use cases, API calls, comparison with FIM, and practical application examples.
Multimodal Agent in Practice: Vision-Driven Automated Operations
Multimodal Development
Advanced
When agents can 'see' screens and images, the boundaries of automation are greatly expanded. This article explains the architecture design of multimodal agents, screenshot understanding, UI element positioning, and complete implementation of action execution, with runnable code.
Reflection and Self-Correction: Doubling AI Output Quality
Agent Development
Intermediate
Reflection is a key technique to improve the quality of AI output. This article explains how to enable AI to self-evaluate, discover errors, and iteratively correct them, significantly improving output quality through a reflection loop.
JSON Mode Structured Output: From Schema to Production
DeepSeek API
Intermediate
DeepSeek V4 natively supports JSON Mode, ensuring model output strictly conforms to JSON Schema. This article details the response_format parameter, structured output validation, Pydantic integration, and production deployment strategies.
Code Agent Engineering: Repo Indexing, Context Collection, and Patch Verification
AI Application Development
Advanced
The hard part of an agent editing a large repository is not writing code, it is knowing what the code means. AST-level indexing that text grep cannot match, seed expansion and layered window budgets across files, reducing requests to the smallest change set, a four-tier verification set, and gating destructive changes such as bulk deletes and dependency upgrades.
DeepSeek-V4.1-Flash Local Deployment & Inference Optimization
Model Deployment
Advanced
Deploy DeepSeek-V4.1-Flash locally: CED architecture, KV Cache 4x compression, quantization, batching, and high-concurrency serving for max throughput.
DeepSeek Data Engineering Tutorial
Model Fine-tuning
Advanced
From data collection to quality evaluation, fully master the large model data engineering pipeline. Covers core aspects such as SFT dataset construction, RLHF preference data preparation, data cleaning and deduplication, data augmentation, and large-scale data processing.
RAG from Beginner to Production: Complete Tech Stack
RAG Retrieval Augmented
Beginner
RAG (Retrieval-Augmented Generation) is the core paradigm for current AI application development. Starting from scratch, this article systematically explains the principles, architecture, and technology stack of RAG, taking you from conceptual understanding to building a usable RAG application.
DeepSeek Enterprise Application Tutorial
AI Application Development
Advanced
DeepSeek enterprise-level application architecture design: multi-tenant isolation, permission management, audit logs, API gateway, and cost control. Includes practical solutions for enterprise RAG systems, intelligent customer service, and knowledge base management, providing an AI implementation guide for large organizations.
RAG Evaluation and Quality Metrics System
RAG Retrieval Augmented
Advanced
Without measurement, there is no improvement. Quality evaluation of RAG systems involves three levels: retrieval quality, generation quality, and end-to-end quality. This article systematically explains core evaluation metrics and frameworks such as Recall@k, MRR, Faithfulness, and RAGAS to help you establish a scientific RAG quality measurement system.
DeepSeek RAG Retrieval-Augmented Tutorial
RAG Retrieval Augmented
Intermediate
Complete tutorial for building a RAG system based on DeepSeek: document loading, text splitting, vector retrieval, re-ranking, and multi-turn dialogue integration. Includes LangChain and LlamaIndex practical code and enterprise-level RAG architecture design.
DeepSeek-R1 Reasoning Model: Principles & CoT Engineering
Model Inference
Advanced
Explore DeepSeek-R1's RL-based training for long chain-of-thought reasoning. Learn CoT prompting, output constraints, temperature tuning, cost control, and evaluation for production.
Agent Security and Permission Control
Agent Development
Intermediate
When AI agents can autonomously call tools, access databases, and execute code, security becomes critical. This article explains agent security architecture design, including sandbox isolation, permission models, audit logs, and injection protection.
DeepSeek Model Introduction
AI Application Development
Beginner
Comprehensive Introduction to DeepSeek Company and Model Ecosystem: Development History, Core Technologies, Model Matrix, and Application Scenarios. Understand how DeepSeek became a leading global AI model provider and how to choose the best DeepSeek model for your needs.
MCP Server Development in Practice: From Protocol to Production-Grade Tool Service
Agent Development
Intermediate
MCP is becoming the standard protocol for AI tool invocation. Starting from protocol principles, this article walks you through implementing a production-grade MCP server, covering tool definition, authentication, error handling, streaming responses, and deployment operations.
Model Routing and Multi-model Orchestration: Choosing by Task, Cost, and Latency
AI Application Development
Intermediate
"Which model should serve this request" is a measurable engineering problem. This guide covers three routes for task classifiers and their failure modes, cold-start label acquisition, the dimensions a capability matrix should track, making cost and latency hard constraints, cross-provider failover, and quantifying routing with oracle upper bounds and Pareto curves.
Connecting Agents to Real Business Systems: SaaS Integration and Transactional Consistency
AI Application Development
Advanced
Once an Agent can write, the hard part is no longer calling the API but undoing, explaining, and tracing a mistake. This article splits permissions along read, write, and cross-tenant dimensions, shows what to do when a vendor ignores your idempotency key, and walks Saga compensation, two-phase commit patterns, human confirmation points, and append-only event logs.
DeepSeek Model Architecture Deep Dive
Model Fine-tuning
Advanced
In-depth analysis of DeepSeek-V3's core technologies such as MoE architecture, MLA attention mechanism, and Multi-Token Prediction. Understand how DeepSeek achieves GPT-4o-level performance at extremely low cost, including architecture diagrams and source code analysis.
Zero-Code Migration: Replacing Claude API with DeepSeek
DeepSeek API
Beginner
DeepSeek natively supports Anthropic API format compatibility; you only need to modify the base_url line to seamlessly migrate Claude applications to DeepSeek. This article details the migration steps, compatibility comparison table, and precautions, achieving cost reduction and efficiency improvement with zero code changes.
Model Distillation in Practice: Training Small Models with Large Models
Model Fine-tuning
Advanced
Knowledge distillation allows small models to approach large model capabilities while significantly reducing inference costs. This article provides an in-depth explanation of distillation principles, data generation strategies, training techniques, and effect evaluation, teaching you to distill cost-effective small models with DeepSeek.
Tool Calls in Practice: The Complete Guide to DeepSeek V4 Function Calling
DeepSeek API
Intermediate
Function Calling is the core mechanism connecting AI to the real world. This article details DeepSeek V4's tool_calls usage, including parallel calls, strict mode, error handling, and multi-tool orchestration, with complete Python/Node.js code.
DeepSeek Harness Sandbox & Approval: Containing Risky Operations
AI Safety
Advanced
How dsh decides if an operation can run: two-stage sandbox decisions and approval flow, read-only/workspace-write/full-access permissions, approval context and timeouts, plus security rules for Plugin developers.
Vector Database Selection in Practice
RAG Retrieval Augmented
Intermediate
Vector databases are the core infrastructure of RAG systems, but with many options like Milvus, Pinecone, Weaviate, Qdrant, and Chroma, how do you make the optimal selection decision? This article comprehensively compares performance, cost, ease of use, and scalability, and provides selection recommendations based on real business scenarios.
Chain-of-Thought Deep Dive
Prompt Engineering
Advanced
Chain-of-Thought (CoT) is not just about adding 'Let's think step by step'. This article deeply analyzes the working mechanism, variant strategies, and cutting-edge progress of CoT from three dimensions: cognitive science, information theory, and engineering practice, helping you truly understand the essence of AI reasoning.
FIM Code Completion: Making DeepSeek Your AI Programming Assistant
DeepSeek API
Intermediate
Fill-in-the-Middle (FIM) is a core technology for code completion. DeepSeek V4 supports native FIM completion and can be integrated into editors such as VS Code and Neovim. This article details FIM principles, API calls, and editor integration.
DeepSeek Getting Started Guide
AI Application Development
Beginner
Getting Started with DeepSeek from Scratch: Register Account, Get API Key, First Conversation, Model Selection, and Pricing. The most comprehensive beginner tutorial, get started with DeepSeek in ten minutes and embark on your AI application development journey.
Plugin Lifecycle & Hot Reload: Fiber State Machine, Nested Plugins, dispose
Agent Development
Advanced
How plugins load and unload: Fiber state machine transitions, nested plugin parent-child isolation, and how old instances settle during hot reload with dispose.
Streaming Output in Practice: SSE Handling and Frontend Rendering
DeepSeek API
Intermediate
In production environments, streaming output is key to improving user experience. This article details DeepSeek API's SSE streaming response handling, including Python/Node.js implementations, frontend word-by-word rendering, thinking mode streaming parsing, and interruption handling.
QLoRA Quantized Fine-Tuning in Practice
Model Fine-tuning
Intermediate
QLoRA reduces the memory requirements for fine-tuning large models to the extreme through 4-bit quantization. This article provides an in-depth explanation of QLoRA's technical principles, NF4 quantization, double quantization, and other key technologies, along with complete practical code.
DeepSeek + LangChain Development Tutorial
AI Application Development
Intermediate
DeepSeek and LangChain Integration Development Tutorial: Chain Construction, Agent Development, Memory Management, and Tool Integration. Includes complete LangChain project practice, with a progressive learning path from basic Chains to complex Agent systems.
工具
Agent Development
Intermediate
在科幻电影《Her》中，AI 助手 Samantha 能主动整理邮件、识别出情感复杂的信件并提议润色回复，能代表主角处理出版事宜，还能在不同的沟通渠道间无缝切换。她的智能之所以动人，是因为她拥有强大的工具——连接语言“大脑”与真实数字世界的“手脚和感官”。今天的 Manus、OpenClaw 等通用 Agent 已经基…
DeepSeek API from Zero to Launch: A Complete Project Walkthrough
DeepSeek API
Beginner
Hands-on guide to building a complete AI application with DeepSeek API—an intelligent customer service system. From environment setup to API calls, from architecture design to frontend interaction, from testing to deployment, covering the entire process.
DeepSeek API Cost Optimization in Practice: Cache Hits Save 98%
DeepSeek API
Beginner
DeepSeek V4 Flash cache hit input costs only ¥0.02/million tokens, saving 98% compared to a miss (¥1/million). This article details the cache mechanism, hit strategies, token management, and a Flash vs Pro selection guide.
Chain-of-Thought (CoT) and Few-shot Prompting Techniques
Prompt Engineering
Intermediate
Chain-of-Thought and Few-shot prompting are key techniques for improving the reasoning ability of large models. This article provides an in-depth explanation of the principles, usage methods, and best practices of these two techniques, helping developers build smarter AI applications.
Large Model Safety and Alignment Techniques
AI Application Development
Advanced
AI safety is the baseline requirement for production deployment. This article provides an in-depth explanation of alignment techniques such as RLHF, DPO, and Constitutional AI, as well as security practices like content moderation and prompt injection defense, to help you build safe and reliable AI applications.
Deep Research Systems in Practice: From Question Decomposition to Cited Reports
AI Application Development
Advanced
The hard part of deep research is not retrieval, it is making every claim trace back to concrete evidence. The design runs from intent clarification and question decomposition through gap-driven query planning, deduplication, evidence quality grading and sentence-level citation alignment, to contradictory evidence, long-report coherence, and end-to-end evaluation.
DeepSeek Harness Built-in Plugins Guide: Categories, Selection & Disabling
Agent Development
Intermediate
Explore dsh built-in plugins by capability: files, Shell, Sandbox, Skills, subagents, workflows, sessions, UI & telemetry. Learn config, selection, and disabling.
DeepSeek Harness Explained: Agent = Model + Harness, Everything Is a Plugin
Agent Development
Beginner
DeepSeek Harness (dsh) is DeepSeek's MIT-licensed Agent runtime. Explore Agent = Model + Harness, its plugin core, four run modes, and how it differs from classic Agent frameworks.
Agent 的评估
Model Evaluation
Intermediate
前六章已经展开了单 Agent 的构建：上下文、知识、工具、代码能力以及观察与动作空间。然而，构建完成不等于构建正确；只有能够稳定测量结果，后续的模型训练和系统进化才有可靠方向。
Prompt Version Management and A/B Testing
Prompt Engineering
Intermediate
Prompts are the soul of AI applications and, like code, require version management and A/B testing. This article explains a complete solution for prompt version control, experiment design, effect evaluation, and progressive rollout.
Test-Driven Development for LLM Applications: Practical Prompt Regression Testing
AI Application Development
Intermediate
Prompts are changed repeatedly, but how can we ensure that production behavior does not regress? This article introduces TDD to LLM applications: from assertion-based unit tests, regression test suites to Golden Sets and evaluation pipelines, building a maintainable AI application quality system.
Introduction to the MCP Protocol: Building Standard Interfaces for AI Tool Calls
Agent Development
Beginner
MCP (Model Context Protocol) is the hottest AI tool calling standard protocol in 2026. This article explains the MCP protocol principles from scratch, Server construction, Client integration, and a complete integration solution with DeepSeek API.
DeepSeek Multimodal Application Tutorial
AI Application Development
Intermediate
In-depth analysis of DeepSeek's multimodal capabilities: visual understanding, image generation, document analysis, and OCR recognition. Includes tutorials for DeepSeek-VL2 and Janus series models, and practical development of multimodal RAG and visual agents.
Build Long-Context Multimodal Agents with DeepSeek-V4.1-Flash
Agent Development
Advanced
Use DeepSeek-V4.1-Flash's 1M context and 384K output to build a multimodal Agent with vision, tool calls, and long-context memory, plus cost and pitfalls.
DeepSeek Multimodal Development in Practice: Janus-Pro Image Understanding and Generation
Multimodal Development
Intermediate
Janus-Pro is DeepSeek's unified multimodal model that supports both image understanding and generation. This article details Janus-Pro-7B's API calls, image question answering, text-to-image generation, and the architecture principle of decoupled visual encoding.
Design of Multi-Agent Communication Protocols
Agent Development
Advanced
When multiple AI agents need to collaborate, communication protocols are the key link connecting them. This article explores the design principles, message formats, routing strategies, and consistency guarantees of multi-agent communication protocols.
AI-Native Product Design: Interfaces That Fit How Models Think
AI Application Development
Intermediate
Streaming output, undo points, and visible reasoning decide how trustworthy an AI product feels. This article compares three streaming strategies and their failure modes, explains partial results and undo points so users can stop early, outlines three levels of thinking visibility, and covers honest uncertainty display and interrupting long tasks.
DeepSeek Fine-tuning Hands-on Tutorial
Model Fine-tuning
Advanced
Full practical guide to fine-tuning DeepSeek models: LoRA/QLoRA efficient fine-tuning, full-parameter fine-tuning, data preparation, training configuration, and evaluation validation. Covers fine-tuning solutions for DeepSeek-V3 and DeepSeek-R1, including complete code and best practices.
Practical Guide to QLoRA Fine-Tuning
Model Fine-tuning
Intermediate
Full-parameter fine-tuning is expensive; QLoRA uses 4-bit quantization and low-rank adaptation to allow ordinary developers to fine-tune large models on consumer-grade GPUs. This article provides a hands-on guide to the complete QLoRA fine-tuning process from data preparation to model deployment, using DeepSeek as the base model.
Model Compression: Where Quantization, Pruning, and Distillation Fit
Model Fine-tuning
Advanced
Quantization, pruning, and distillation are often compared in isolation, so it's hard to tell whether a compression win is real or just moved cost. This article puts them on one ledger: bit width, grouping and outliers for weights, how GPTQ and AWQ treat error propagation differently, where KV Cache quantization and FP8 fit, and structured versus unstructured pruning.
Model Evaluation and A/B Testing
Model Fine-tuning
Advanced
After fine-tuning, how can you objectively evaluate model performance? How should A/B tests be designed to obtain statistically significant conclusions? This article establishes a scientific model evaluation system from three dimensions: offline evaluation, online A/B testing, and statistical significance.
DeepSeek Harness Plugin DI, Effects & Service Model
Agent Development
Advanced
Inside the dsh plugin kernel: inject dependency timing, ctx.effect auto-tracking and reverse cleanup, and Service registration. Practical patterns for dependency cycles, optional deps, and service replacement.
AI Agent Architecture Design: From ReAct to Plan-and-Execute
Agent Development
Intermediate
AI Agent is one of the hottest technology directions currently. This article provides an in-depth analysis of mainstream Agent architectures such as ReAct, Plan-and-Execute, and Multi-Agent, helping you understand the applicable scenarios and design principles of different architectures.
Long-Context Architecture: RoPE Extrapolation, Sparse Attention, and KV Cache
Model Architecture
Advanced
A context window looks like one number, but it is really two: what you can feed in and what the model can effectively use. Three levers decide whether long context holds — RoPE extrapolation, attention sparsity, and the KV Cache lifecycle from memory accounting to quantization and eviction — plus how Prefill differs from Decode, and where each trick stops working.
Multimodal Model Architecture: Vision Encoders, Projection Layers, and Modality Alignment
Multimodal Development
Advanced
How does a vision-language model turn an image into tokens it can talk about? Following the data flow, this guide weighs CNN against ViT encoders, resolution against the visual token budget, and linear, MLP and query-based projection layers, then explains what visual alignment pretraining, instruction tuning and preference optimization each learn.
RAG Performance Optimization: From Retrieval Accuracy to End-to-End Latency
RAG Retrieval
Intermediate
Struggling with inaccurate retrieval and slow responses in RAG? This article covers two-hop retrieval, hybrid search, and reranking to boost accuracy, plus caching, parallel chunking, and semantic cache to cut latency, with quantifiable metrics and a tuning checklist.
RLHF Reinforcement Fine-Tuning in Practice: From Preference Data to Reward Model
Model Fine-tuning
Advanced
RLHF aligns model outputs with human preferences. This article walks through the complete RLHF pipeline, from preference data collection and reward model training to PPO reinforcement fine-tuning, and provides engineering insights on data quality and effect evaluation.
Design of AI Writing Assistant Tools
AI Application Development
Beginner
AI writing assistance is a productivity multiplier for content creators. This article designs an AI writing assistance tool from scratch that supports multiple writing modes (article continuation, style polishing, outline generation, and summary extraction), using the DeepSeek API for streaming output and context management.
DeepSeek V4 Flash Performance In-Depth Review
DeepSeek API
Intermediate
DeepSeek V4 Flash rivals GPT-5 performance at an ultra-low price of ¥0.02/M tokens. This article provides objective selection advice based on real tests across six dimensions: coding, reasoning, mathematics, multilingual, latency, and concurrency.
Function Calling in Practice: Let AI Invoke External Tools
Agent Development
Beginner
Function Calling is the bridge connecting AI to the real world. Starting from scratch, this article explains the principles, implementation methods, and common application scenarios of Function Calling, enabling AI to query databases, send emails, and call APIs.
Multimodal AI Application Development in Practice
AI Application Development
Intermediate
Multimodal AI is changing the way humans interact with machines. This article explains the usage of vision-language models (VLMs), image-text understanding and generation, multimodal RAG, and the construction of multimodal agents, enabling you to master the complete skills of multimodal AI development.
DeepSeek Prompt Engineering Tutorial
Prompt Engineering
Intermediate
DeepSeek-specific prompt engineering guide: chain-of-thought reasoning, few-shot learning, structured output, and function calling. Covers prompt templates and optimization strategies for scenarios such as mathematical reasoning, code generation, and text analysis.
Getting Started with Multimodal RAG
RAG Retrieval Augmented
Beginner
Traditional RAG can only handle text, but real-world data is far more than text. Multimodal RAG incorporates non-text data such as images, tables, and audio into the retrieval-augmented scope, enabling AI to understand richer information forms. This article takes you from scratch to understand the design philosophy and implementation path of multimodal RAG.
Efficient Use of 1M Context: Strategies for Multi-Turn Dialogues and Long Document Processing
DeepSeek API
Intermediate
DeepSeek V4 supports a 1M token context window (approximately 700,000 Chinese characters). This article details how to efficiently utilize long contexts, including message truncation strategies, summary compression, chunked processing, and best practices for large document analysis.
LangChain Basics: Building Your First AI Application
AI Application Development
Beginner
LangChain is currently the most popular AI application development framework. This article starts from scratch, guiding you through LangChain's core concepts and building a complete AI application hands-on—from environment setup to feature implementation.
Multi-Agent Collaboration Patterns in Practice: From Orchestration to Autonomy
Agent Development
Advanced
Single agents have limited capabilities; multi-agent collaboration is key to unlocking complex tasks. This article starts from practical experience, compares mainstream collaboration modes such as Supervisor, GroupChat, and Sequential, and provides runnable code implementations.
AI-Driven Data Analysis Pipeline
AI Application Development
Advanced
Traditional data analysis relies on extensive manual coding, while AI-driven data analysis pipelines make natural language the interface for data queries. This article builds a complete data analysis pipeline with NL2SQL + automatic visualization + insight generation, allowing anyone to explore data using natural language.
DeepSeek Model Download and Installation
Deployment and Operations
Beginner
Complete Guide to DeepSeek Model Download and Installation: Hugging Face Download, Ollama One-Click Deployment, Quantization Version Selection, and VRAM Requirements Assessment. Includes download links, installation steps, and verification methods for all DeepSeek models.
Enterprise AI Middleware Architecture Design
AI Application Development
Advanced
When an enterprise needs to manage dozens of AI services in a unified manner, the middleware layer is crucial. This article explains the complete architecture design of AI gateways, rate limiting, load balancing, model routing, and monitoring.
AI-Driven Automated Testing Systems
AI Application Development
Intermediate
Using AI to automatically generate test cases, detect bugs, and generate test reports greatly improves software testing efficiency. This article explains the architecture design and core implementation of an AI testing system.
DeepSeek Agent Development Tutorial
Agent Development
Intermediate
Complete tutorial for building AI agents based on DeepSeek: ReAct mode, tool calling, memory management, and multi-agent collaboration. Includes practical code for LangChain Agent, AutoGPT-style autonomous agents, and Function Calling Agent.
Define Tools in DeepSeek Harness: Schema, Approval & Pipeline
Agent Development
Advanced
Register a tool with ctx and make models call it reliably: parameter schema, approval hooks, return values, and the fixed dsh tool pipeline order.
AI Data Compliance and Privacy Computing: An Engineering Guide
AI Safety
Advanced
Data classification and minimization become gates; masking, pseudonymization, and anonymization diverge legally; k-anonymity and t-closeness buy and cost; TEE, homomorphic encryption, federational learning, and secret sharing differ in trust; RAG vector stores and retrieval logs leak; deletion rights, cross-border transfer, audit logs; memorization risk in fine-tuning.
The Art of Building Fine-Tuning Datasets
Model Fine-tuning
Intermediate
The upper limit of fine-tuning effectiveness is determined by data quality, not the model or algorithm. This article delves into the methodology for building fine-tuning datasets, covering data sources, quality control, diversity assurance, and data mixing strategies, helping you build high-quality fine-tuning datasets.
RAG Re-ranking and Hybrid Search in Practice: From BM25 to Cross-Encoder
RAG Retrieval Augmented
Intermediate
Vector retrieval alone has limited recall; reranking is a key step in improving RAG effectiveness. This article systematically explains BM25 sparse retrieval, vector retrieval with RRF fusion, and complete practices for Cross-Encoder reranking.
AI Application Observability: Logging, Monitoring, and Alerting in Practice
Deployment and Operations
Intermediate
Production AI applications require a comprehensive observability system. This article details token usage monitoring, API latency tracking, error rate alerts, cost analysis dashboards, and logging best practices to help you build reliable AI services.
上下文工程
Agent Development
Intermediate
第一章把上下文比作 Agent 的“眼睛”——Agent 只能基于它看到的信息做决策。上下文的设计和管理称为上下文工程（Context Engineering）。所谓上下文，就是每次你和 AI 对话时，AI 实际“看到”的全部信息。它不仅包含你们之前聊了什么（对话历史），还包含开发者预先写好的行为规则（系统指令）、AI…
Inference Engine Internals: Continuous Batching, Prefix Cache, Speculative Decoding
Deployment and Operations
Advanced
Throughput and tail latency pull against each other, and the tension comes down to scheduling and memory. Why static batching wastes capacity, what Continuous Batching and chunked prefill cost to build, how PagedAttention pages memory, when a high Prefix Cache hit rate backfires, and why the acceptance rate of Speculative Decoding sets your speedup.
Prompt Compression and Semantic Caching Engineering in Practice
Prompt Engineering
Intermediate
Overly long prompts increase costs and reduce response speed. This article provides an in-depth explanation of prompt compression techniques and semantic caching practices, teaching you how to compress repetitive and lengthy prompts by over 60% and speed up similar requests by 10x through cache hits.
DeepSeek Deployment and Operations Tutorial
Deployment and Operations
Intermediate
Complete DeepSeek model deployment process: vLLM deployment configuration, load balancing, auto-scaling, monitoring and alerting, and cost optimization. Includes Docker and Kubernetes deployment solutions and cloud platform deployment guides, covering all scenarios from single machine to cluster.
Distributed Training of Large Models: Data, Tensor, Pipeline, and Expert Parallelism
Model Fine-tuning
Advanced
A single GPU cannot hold the model, so parallelism has to be chosen deliberately. Data, tensor, pipeline and expert parallelism are compared by communication pattern and usable scale: why tensor parallelism needs two all-reduces per layer, how communication grows with parallelism degree, how NVLink and cross-node bandwidth cap the choices, and what ZeRO 1/2/3 saves.
Structured Prompt Design Patterns
Prompt Engineering
Intermediate
Prompting is not just about writing a few instructions to get high-quality output. This article systematically explains six proven structured prompt design patterns, including role pattern, template pattern, constraint pattern, step-by-step pattern, reflection pattern, and combination pattern, helping you upgrade prompt engineering from intuition to methodology.
模型后训练
Model Fine-tuning
Intermediate
本书的核心公式是 Agent = LLM + 上下文 + 工具。本章聚焦于优化 LLM 这个“大脑”——先用 Mid-training 补齐目标领域的知识与基础能力，再用 SFT/RL 等后训练方法塑造模型利用上下文和工具的行为。第七章结尾指出，评估体系与仿真环境是后训练的两块基石：评估环境为训练提供练习场，评估指标为…
DeepSeek Performance Tuning Tutorial
Deployment and Operations
Advanced
Complete DeepSeek performance tuning guide: GPU memory optimization, batching strategies, quantization techniques, concurrency control, and latency optimization. Includes profiling tool usage, performance benchmarking, and best practices to achieve an optimal balance between throughput and latency.
Vector Database Selection: Chroma vs Pinecone vs Milvus
RAG Retrieval Augmented
Intermediate
Vector databases are core components of RAG systems. This article compares three mainstream vector databases—Chroma, Pinecone, and Milvus—from four dimensions: performance, ease of use, cost, and scalability, helping you make the right selection decision.
AI Testing in CI/CD Pipelines
Deployment and Operations
Intermediate
Integrating AI model testing into CI/CD pipelines is key to continuously delivering high-quality AI services. This article explains a complete solution for automated evaluation, regression testing, performance benchmarks, and deployment gates.
DeepSeek Harness Hands-On Review: Setup, Modes & Token Usage
AI Application Development
Beginner
A full hands-on guide to dsh: 5-minute install, Web UI setup, four run modes, directory permissions, Token usage and Trajectory review, plus common pitfalls.
Best Practices for System Prompt Engineering
Prompt Engineering
Intermediate
System prompts are a key mechanism for controlling AI behavior. This article explains the design principles, role setting, constraint expression, structured output, and common pitfalls of system prompts, helping you write more precise prompts.
LLM Adapter & StreamChunk: Stream Any Model into DeepSeek Harness
DeepSeek API
Advanced
Build an LLM adapter for DeepSeek Harness: minimal interface, StreamChunk field semantics, chunk ordering, reasoning and tool calls in one stream, plus incremental rendering and retry.
Building a Smart Customer Service Agent from Scratch
AI Application Development
Intermediate
Intelligent customer service is one of the most widespread AI application scenarios. This article builds an intelligent customer service Agent from scratch with intent recognition, knowledge retrieval, ticket creation, sentiment analysis, and human-machine collaboration capabilities, fully covering the core needs of customer service scenarios.
RAG Agent Application Implementation: From Knowledge Base to Autonomous Decision-Making
AI Application Development
Intermediate
The combination of RAG and Agents is reshaping enterprise knowledge applications. This article builds a production-grade RAG Agent step by step, from knowledge base construction, retrieval augmentation, tool calling to autonomous decision-making, and discusses evaluation and iteration methods.
Structured Output and JSON Mode in Practice
Prompt Engineering
Advanced
In production environments, unstructured output from AI is difficult to use directly. This article provides an in-depth explanation of how to make large models output structured data, including JSON Schema constraints, Pydantic integration, and best practices for structured output.
Deep Optimization of Document Chunking Strategies
RAG Retrieval Augmented
Intermediate
Document chunking is the most underestimated critical step in RAG systems. The quality of the chunking strategy directly affects retrieval quality and the accuracy of the final answer. This article deeply analyzes strategies such as fixed-size chunking, semantic chunking, recursive chunking, and sentence-level chunking to help you find the optimal solution.
DeepSeek Inference Acceleration In-Depth Tutorial
Deployment and Operations
Advanced
Comprehensively master DeepSeek model inference acceleration techniques. In-depth comparison of four major engines: vLLM, SGLang, TensorRT-LLM, and llama.cpp, from deployment to tuning, from single GPU to distributed, providing a complete inference acceleration solution.
Constrained Decoding and Structured Output: Grammar, JSON Schema, and Sampling Repair
AI Application Development
Intermediate
Prompting a model to emit JSON is not a guarantee. Three layers make output reliably parseable: per-token masking that admits only legal tokens, compiling JSON Schema into a decoding grammar, and a post-sampling repair layer with validation and bounded retries. It also covers joint constraints for tool calls and structured edits, per-layer latency, and a launch checklist.
LLM Inference Performance Optimization: vLLM and Continuous Batching
Deployment and Operations
Advanced
After model deployment, throughput and latency are core metrics. This article provides an in-depth analysis of vLLM's PagedAttention and Continuous Batching principles, and gives a complete optimization roadmap from benchmark testing, parameter tuning to production deployment.
DeepSeek Model Evaluation and Benchmarking
Model Fine-tuning
Intermediate
Complete evaluation guide for DeepSeek models: results on mainstream benchmarks such as MMLU, HumanEval, GSM8K, and MT-Bench. Comprehensive comparison of DeepSeek-V3 vs GPT-4o vs Claude 3.5, including evaluation code and model selection recommendations.
Agent Skills Guide: SKILL.md, Progressive Disclosure & Scripts
Agent Development
Intermediate
A complete guide to Agent Skills: SKILL.md YAML metadata, progressive disclosure and Token budget, deterministic scripts vs prompts, MCP boundaries, and security audit.
Token Usage Precision and Rate Limiting Strategies
DeepSeek API
Beginner
Accurately calculating token usage is the foundation for controlling API costs. This article details token calculation methods, DeepSeek rate limiting rules, concurrency control, and request queue design in production environments.
Panorama of Large Model Inference Acceleration Technologies
Deployment and Operations
Intermediate
Inference speed directly determines user experience. This article systematically explains inference acceleration techniques such as KV Cache, Speculative Decoding, model quantization, and operator fusion, helping you comprehensively master large model inference optimization from principles to practice.
Agent 的持续进化
Agent Development
Intermediate
今天的 Agent 面临一个鲜明的能力悖论：它可以零样本解决从未见过的复杂任务，却可能在处理了一万次相似任务之后，第二天仍然犯下第一天的错误。模型上岗之后，能否像新员工一样从日常工作中不断长进？能否自主从经验中学习（所谓持续学习，continual learning），正在成为 Agent 从“会完成任务”走向“能够可…
AI Application Performance Optimization and Cost Control
AI Application Development
Advanced
The performance and cost of AI applications directly impact product experience and commercial viability. This article systematically explains methods for AI application performance optimization and cost control from dimensions such as prompt optimization, caching strategies, model selection, and architecture design.
Agent Testing and Debugging Strategies
Agent Development
Intermediate
The non-deterministic behavior of agents makes traditional testing methods ineffective. This article introduces specialized testing strategies for agents, including evaluation-driven testing, observability debugging, and CI/CD integration solutions.
DSH Plugin Release, Defensive Programming & Incident Review
Deployment and Operations
Advanced
Three ways to distribute DSH plugins, version conventions, pre-release checks, five defensive patterns, and the dsh incident review discipline for your team.
Embedding Model Selection and Vector Quality Evaluation: Dimensions, Domains, and Methodology
RAG Retrieval Augmented
Intermediate
"Swap in a different embedding model" is not evaluation. This guide starts from the trade-off between dimensions, storage and latency, then covers the limits of general benchmarks like MTEB and why task-level evaluation is unavoidable: building a small domain eval set, rerankers versus embedding models, and how HNSW parameters or quantization can mask model quality.
Prompt Engineering Basics: From Zero to Mastery
Prompt Engineering
Beginner
Prompt engineering is a fundamental skill for AI application development. Starting from basic concepts, this article systematically explains the components, design principles, and optimization methods of prompts, helping beginners quickly master the core techniques of effective communication with AI.
Multi-Agent Collaboration: Building an AI Team
Agent Development
Advanced
A single Agent has limited capabilities; multi-Agent collaboration can solve more complex problems. This article explains the design patterns, communication mechanisms, and practical application cases of multi-Agent systems, teaching you to build AI teams that can work collaboratively.
Building an Enterprise-Grade AI Knowledge Base System from Scratch
RAG Retrieval Augmented
Beginner
Enterprise knowledge bases are the best entry point for AI implementation. This article comprehensively explains how to build an enterprise-level AI knowledge base system that supports multi-format documents, permission management, and continuous updates, from requirements analysis to system deployment.
Practical Model Quantization and Deployment
Model Fine-tuning
Intermediate
Quantization is a key technology to reduce model inference costs. This article systematically explains the principles, toolchains, and deployment practices of model quantization from INT8 to INT4, enabling your model to run efficiently on consumer-grade hardware.
AI Application Cost Engineering: Token Ledgers, Unit Economics, and Cost Governance
Deployment and Operations
Intermediate
Turn a month-end invoice into a daily operational metric. This guide covers a cost event schema and ledgers by feature, tenant and model, cost per session and effective tokens, a unit economics model built from contribution margin, what prompt caching, quantization, small-model routing and batching actually save once corrected for success rate, and nine cost attribution traps.
Detailed Explanation of DPO Preference Alignment Training
Model Fine-tuning
Advanced
DPO (Direct Preference Optimization) is a simplified alternative to RLHF that does not require training a reward model. This article explains the principles, implementation details, and training techniques of DPO in depth.
AI Agent 入门
Agent Development
Intermediate
如果你用 Cursor 写过代码，看它搜索代码库、编辑多个文件、运行测试直到通过；用 Deep Research 调研过一个课题，看它反复搜索、阅读，总结出一份完整报告；用 Manus 操控浏览器帮你完成在线任务；让豆包手机助手帮你在手机上订票、发消息；或者让 Pine AI 替你打电话给运营商协商降低账单——你已经在…
Design of Agent Memory Systems
Agent Development
Advanced
Memory is the key to transforming agents from one-time tools to long-term intelligent companions. This article deeply analyzes the architecture design of agent memory systems, covering short-term/long-term memory, RAG-enhanced memory, and hybrid retrieval strategies.
Multi-turn Dialogue Prompt Strategies
Prompt Engineering
Intermediate
Multi-turn dialogue is the most common interaction form in AI applications, but maintaining the quality and consistency of long conversations is a huge challenge. This article deeply analyzes core technologies such as context window management, memory compression, topic tracking, and dialogue state management to help you build smooth and intelligent multi-turn dialogue systems.
AI Application Security and Content Moderation Best Practices
AI Safety
Intermediate
After an AI application goes live, security is an aspect that cannot be ignored. This article details prompt injection protection, sensitive word filtering, jailbreak attack defense, API key management, and content moderation strategies to help build secure AI services.
DeepSeek Harness Setup Guide: Install & Configure dsh
Deployment and Operations
Beginner
Run dsh from scratch: npx and global install, Web UI setup, working directory, four run modes, plus DeepSeek, GLM, Qwen, MiMo and Volcengine model config.
DeepSeek-V4.1-Flash: CED Asymmetric Architecture & KV Cache Compression
Model Architecture
Advanced
DeepSeek-V4.1-Flash redefines Agent-era inference with a 552B MoE and CED asymmetric architecture. We break down KV Cache compression and its cost model.
Plugin Config & Bundle/Profile Layers: How dsh Resolves Effective Config
Deployment and Operations
Advanced
How dsh layers plugin schema defaults, Profile, Bundle, user overrides and CLI patches. Use dsh --dump-config to trace effective values, manifest roles and override priority.
Canary Releases for AI Apps: Model, Prompt, and Retrieval Changes Together
Deployment and Operations
Intermediate
When one release changes the model, the prompt, and the retrieval layer at once, attribution gets harder than rollback. This article freezes any request into a content-addressed triple, separates what shadow traffic and A/B splits can and cannot answer, and outlines regression gates covering structure, semantics, retrieval, cost, and latency.
多 Agent 协作
Agent Development
Intermediate
前九章围绕单个 Agent 展开：先构建上下文、知识、工具与交互能力，再通过评估、后训练和持续进化让它长期变好。本章把问题从“如何构建和改进一个 Agent”推进到“如何组织多个 Agent”——让它们通过分工、通信与相互验证完成单个 Agent 难以承担的任务。
Sessions, Turns & Trajectory Replay: DeepSeek Harness Observability
Agent Development
Intermediate
Inside dsh's session data model: session/turn/step hierarchy, append-only event logs, and what the Trajectory view reveals for recovery, forking, and cost attribution.
Agent Protocols Explained: Where MCP, A2A, and AG-UI Each Belong
Agent Development
Advanced
Three agent protocols answer three different questions. MCP standardizes how tools and context are exposed, A2A covers discovery and delegation between agents, AG-UI streams a running agent to the frontend. It also shows a bridge in code — a remote A2A agent wrapped as an MCP tool — plus capability negotiation, version drift, mismatched error semantics, and cancellation.
Agent Memory System Design: Short-Term, Long-Term, and Tool Memory
Agent Development
Advanced
Memory is the foundation of Agent intelligence. This article models four layers: working, episodic, semantic, and tool memory, covering compression, retrieval, writing, and forgetting strategies. Implement a complete Agent memory subsystem with vector memory and reflection using DeepSeek API.
DeepSeek API Quick Start: From Registration to First Application
AI Application Development
Beginner
DeepSeek provides cost-effective LLM API services. This article starts from scratch, guiding you through DeepSeek API registration, key acquisition, SDK installation, and building your first AI application—all within ten minutes.
Voice AI Application Development in Practice
AI Application Development
Intermediate
Voice interaction is becoming a new entry point for AI applications. This article starts from the complete chain of ASR speech recognition, LLM dialogue generation, and TTS speech synthesis, and builds an end-to-end voice AI application, covering real-time streaming processing and latency optimization.
后记：回到 Agent = LLM + 上下文 + 工具
Agent Development
Intermediate
本书开篇提出了一个公式：Agent = LLM + 上下文 + 工具。全书十章，都在这三个词里展开。
Best Practices for Large Model API Service
Deployment and Operations
Advanced
API-fying LLM capabilities is a key step for enterprise-level applications. This article systematically explains best practices for LLM API services from dimensions such as architecture design, rate limiting strategies, cache optimization, and security protection.
DeepSeek API in Action: From Getting Started to Production Deployment
AI Application Development
Intermediate
In-depth understanding of DeepSeek API features, from basic calls to production best practices. Covers core topics such as streaming output, Function Calling, rate limits, error retries, and multi-model switching.
GPU Cluster Management and Scheduling
Deployment and Operations
Advanced
Managing a cluster of dozens of GPUs requires professional scheduling strategies. This article explains practical experience in GPU resource management, task priority scheduling, memory fragmentation cleanup, and multi-tenant isolation.
In-Depth Guide to GPU-Accelerated Inference Optimization
Deployment and Operations
Advanced
GPU inference optimization is key to reducing the cost of large model deployment. This article provides an in-depth explanation of core technologies such as FlashAttention, quantized inference, TensorRT acceleration, and continuous batching to help you maximize GPU utilization.
Prompt Security and Injection Defense
Prompt Engineering
Advanced
Prompt injection is the primary security threat to AI applications. Attackers can bypass system instructions, steal sensitive information, and induce malicious behavior through carefully crafted inputs. This article explains in depth the attack vectors, detection methods, and defense strategies of prompt injection to help you build secure AI applications.
World Models and Embodied AI: Technical Routes and Engineering Challenges
Multimodal Development
Advanced
A world model lets a robot rehearse the consequences of an action before taking it. This article compares representational, generative, and latent-space prediction routes, then digs into metric versus topological 3D representation, error that accumulates faster than linearly over long horizons, and the cost of real robot data.
Monitoring and Observability for AI Applications
Deployment and Operations
Advanced
Observability for AI applications is far more complex than for traditional applications—it requires monitoring multiple dimensions such as LLM call latency, token consumption, hallucination rate, and user satisfaction. This article builds a complete AI application monitoring system, covering metric collection, alert configuration, cost tracking, and quality monitoring.
Advanced RAG: Multi-hop Retrieval and Self-Query
RAG Retrieval Augmented
Advanced
Basic RAG cannot handle complex problems that require cross-document reasoning. This article provides an in-depth explanation of advanced RAG techniques such as multi-hop retrieval, self-query retrieval, and context compression, helping you elevate your RAG system to production-grade level.
AI Search Architecture: Engineering Trade-offs in Retrieval, Reranking, and Generation
AI Application Development
Advanced
Every layer of AI search has a trade-off: forward, inverted, and vector indexes cost storage; multi-path recall fuses sparse and dense hits, trading recall for latency; query understanding handles intent, rewrite, freshness, multi-turn context; reranking buys precision at a price; generation brings hallucination and citation tracing; offline and online metrics drift apart.
Dataset Construction and Quality Evaluation
Model Fine-tuning
Advanced
Data quality determines the upper limit of fine-tuning effectiveness. This article systematically explains the construction methods of fine-tuning datasets, data cleaning strategies, quality evaluation metrics, and data augmentation techniques, helping you build high-quality fine-tuning datasets.
Building AI Applications with Dify Low-Code Platform
AI Application Development
Beginner
Dify is an open-source low-code AI application development platform that enables users without programming knowledge to quickly build AI applications. This article comprehensively explains how to use Dify, from installation and deployment to application release.
Capability Seams in DeepSeek Harness: Build Custom Agent Extensions
Agent Development
Advanced
Learn how Capability Seams extend dsh: definer, provider, and consumer roles, three-layer events in the Agent loop, and what to deliver when building a custom capability.
Build Your First DeepSeek Harness Plugin: apply, ctx & cordis.yml
Agent Development
Intermediate
Write a runnable dsh plugin from scratch: TypeScript module exporting name and apply, ctx registration semantics, scratch-plugin layout, and loading it via cordis.yml insert and --patch.
Building an AI Code Review System
AI Application Development
Intermediate
Code review is an indispensable part of software development, but manual review is time-consuming and inconsistent in quality. This article teaches you step by step how to build an automated code review system using the DeepSeek API, covering security vulnerability detection, performance analysis, code style checking, and best practice suggestions.
Getting Started with Low-Code AI Application Development
AI Application Development
Beginner
You don't need deep programming skills to build powerful AI applications. This article introduces how to quickly build RAG applications, agents, and automated workflows using low-code platforms such as Dify and Flowise.
Multi-Agent Collaboration: Building an AI Team with Skill Chains
Agent Development
Advanced
A single AI has limited capabilities, but collaboration among multiple AI roles can solve complex problems. This article explains how to orchestrate multiple Agents with skill chains, building a collaborative team of planner → executor → reviewer.
DeepSeek-V3 vs R1: Complete Model Selection Guide (2026 Latest)
AI Application Development
Beginner
DeepSeek-V3 and R1 are the two most popular model series currently. This article compares them from multiple dimensions such as performance, cost, applicable scenarios, and token consumption to help you make the best selection decision.
AI Agent Red Teaming and Security Assessment Framework
AI Safety
Advanced
Agents that can call tools are at risk of being exploited. This article systematically explains Agent red team testing methodologies, covering attack surfaces such as prompt injection, unauthorized tool calls, supply chain pollution, as well as supporting automated evaluation and defense systems.
Best Practices for Containerizing AI Services
Deployment and Operations
Intermediate
Containerizing AI model services is the first step in production deployment. This article explains a complete solution for Docker image optimization, GPU support, health checks, and Kubernetes deployment.
Long-Running Agent Engineering: Task Graphs, Resumption, and Cost Control
Agent Development
Advanced
When an Agent works for hours, the bottleneck is not how long the model thinks but whether state survives a process restart. Task graph persistence, checkpoint granularity by side-effect density, idempotency with intent logs, lease-guarded resumption, precondition fingerprints for invalidation, context compaction, externalized memory, and cost gates with human checkpoints.
AI Agent Tool Development in Practice: From Design to Deployment
Agent Development
Intermediate
Tools are the hands and feet of agents. This article provides an in-depth explanation of how to design, develop, test, and deploy high-quality AI agent tools, including tool interface design, error handling, performance optimization, and security protection.
Agent Loop Basics: Understanding AI Autonomous Workflows from Scratch
Agent Development
Beginner
Agent Loop is the hottest AI development paradigm today. Starting from the ReAct pattern, this article helps you understand how AI achieves the autonomous loop of observation → planning → execution → feedback, and master the core concepts of Agent development.
Agent Reliability Engineering: Timeouts, Retries, Idempotency, and Fault Isolation
Agent Development
Advanced
Treat an Agent as a distributed system that will fail. This guide covers inheriting a wall-clock budget as context rather than resetting it per layer, why retries require idempotency, binding idempotency keys to intent, compensating irreversible side effects with a Saga ledger, and proving these mechanisms with chaos tests.
SKILL.md Guide: Fields, 25+ Platform Paths & Token Budget
Agent Development
Intermediate
A practical SKILL.md handbook: YAML frontmatter rules, install paths for 25+ platforms, progressive loading Token budgets, and Skill vs MCP roles.
Agentic RAG: Let AI Retrieve Autonomously
RAG Retrieval Augmented
Advanced
Traditional RAG is passive—the system retrieves what the user asks. Agentic RAG gives AI autonomous decision-making ability, enabling it to proactively determine whether retrieval is needed, what to retrieve, and how to integrate multiple retrieval results. This article deeply analyzes the architecture design and implementation path of Agentic RAG.
AI API Cost Optimization in Practice
Deployment and Operations
Intermediate
The token cost of AI APIs may seem cheap, but costs can spiral out of control at scale. This article systematically explains practical strategies for optimizing AI API costs, covering prompt compression, semantic caching, model routing, and batch processing, helping you reduce your monthly bill by 50%-80%.
Advanced Prompt Engineering: Structured Prompts, CoT, Self-Reflection
AI Application Development
Intermediate
Explore advanced prompt engineering paradigms: structured prompts, chain-of-thought, self-reflection, and least-to-most. Includes DeepSeek API examples and evaluation sets for measurable, iterative optimization.
DeepSeek Safety and Content Moderation
AI Application Development
Advanced
Production environment security protection guide: from prompt injection defense to content moderation systems, from jailbreak detection to red team testing. Complete security architecture design and Python implementation code to ensure AI applications are safe and reliable.
Design of Multilingual RAG Systems
RAG Retrieval Augmented
Intermediate
Building a RAG system that supports multiple languages such as Chinese, English, Japanese, and Korean presents unique challenges. This article explains a complete solution for multilingual embeddings, cross-lingual retrieval, translation strategies, and performance optimization.
Coding Agent 与通用 Agent
Agent Development
Intermediate
前面的章节分别深入讨论了上下文工程（第二、三章）和工具设计（第四章）。本章将这些构件组合在一起，回答一个核心问题：一个能处理任意任务的通用 Agent，它的架构长什么样？
Designing Large-Scale Vector Search: From HNSW to Distributed Indexing
Retrieval Systems
Advanced
Learn to build a scalable vector search foundation for tens of millions of vectors with millisecond recall. Covers HNSW graph indexing, IVF-PQ quantization, memory/SSD tiering, sharding, replication, cascaded retrieval, caching, architecture comparisons, and load testing.
AI Workflow Engine in Practice: From Scripts to Visual Orchestration
AI Application Development
Intermediate
Complex AI applications should not be a pile of coupled scripts. This article explains how to build a lightweight AI workflow engine, including node abstraction, conditional branching, loop execution, and breakpoint recovery, with a complete runnable Python implementation.
Multimodal Document Understanding: From Layout Parsing to Semantic Retrieval
Multimodal Development
Advanced
Document understanding is neither OCR nor feeding images to a multimodal model. Following the real pipeline order: where the classic four-stage pipeline splits from the end-to-end image reader, restoring tables with merged cells, whether a chart becomes data or a question, which stage breaks first on a degraded scan, and how layout-level and end-to-end metrics differ.
Hands-on: Build Your First Agent Loop Application
Agent Development
Advanced
From theory to practice, this article teaches you step by step how to build a complete Agent Loop application using the DeepSeek API. It includes the complete process of requirements analysis, task planning, step-by-step execution, and result verification.
DeepSeek-V4.1-Flash Benchmarks & Model Selection Guide
Model Evaluation
Intermediate
Decode DeepSeek-V4.1-Flash benchmarks: GPQA Diamond 90.9, Codeforces 3471, CyberGym 88.1. Compare V4 Pro vs Flash and get a model selection framework.
Tool Calling Loop: Enabling AI to Use External Tools
Agent Development
Intermediate
Function Calling is a key capability of Agent Loop. This article explains in depth how AI autonomously selects tools, calls APIs, and parses results to build truly capable AI assistants.
DeepSeek Open Source Project Selection: Top 10 Tools Worth Following on GitHub
AI Application Development
Beginner
There are many excellent open-source projects based on DeepSeek on GitHub. This article selects 10 of the most noteworthy tools and frameworks, covering directions such as chat clients, API proxies, Agent frameworks, and RAG systems, to help you quickly find the right open-source solution for your project.
交互：观察与动作空间的扩展
Agent Development
Intermediate
第一章提出过一个论断：在底层模型固定时，提升 Agent 任务表现最主要的系统工程手段，往往是重新定义或扩展观察空间与动作空间。在前五章的内容中，Agent 与世界的交互是轮流的。用户说完一句，Agent 想一段、调用几个工具，再回一句；在它思考的这段时间里，世界被默认为静止的。这个前提如此自然，以至于很少被当成一个假…
DeepSeek-Flash API Migration: From V4 Pro, Cost-Optimized
API Development
Intermediate
V4 Pro retires Sept 14. Migrate smoothly to deepseek-flash with compatible routing, OpenAI/Anthropic formats, Thinking mode, vision, Tool Calls, and cost optimization.
Long Context in Practice: KV Cache and 1M Token Application Design
DeepSeek API
Intermediate
The 1M context makes it possible to 'stuff in an entire manual', but processing long contexts has many engineering pitfalls. This article explains KV Cache mechanisms, long document chunking strategies, token budget management, and practical designs for multi-turn conversations.
Prompt Injection Defense and Security Hardening
Prompt Engineering
Advanced
Prompt injection is the number one security threat facing large model applications. This article provides an in-depth analysis of various injection attack principles and offers systematic defense strategies, including input filtering, output validation, sandbox isolation, and permission control.
In-Depth Implementation of ReAct Agents
Agent Development
Advanced
ReAct (Reasoning + Acting) is one of the most classic paradigms in agent development. This article explains in depth how to build a production-grade ReAct agent, from principles to implementation, including prompt engineering, tool management, and error recovery.
Comprehensive Comparison of LoRA vs QLoRA vs Full Fine-tuning
Model Fine-tuning
Intermediate
A comprehensive comparison of LoRA, QLoRA, and full fine-tuning, analyzing from the perspectives of principles, memory usage, training speed, and final performance, to help you make the right technology choice.
Test-Time Compute Scaling: Reasoning Budgets, Sampling, and Parallel Search
Model Inference
Advanced
Frozen weights plus extra compute has a marginal-return curve nothing like training's. It covers defining and splitting the reasoning budget, parallel sampling with self-consistency, verifier-guided Best-of-N reranking, process reward versus outcome reward, depth-first and breadth-first search topologies, compute-optimal scaling, plus production budget caps and timeouts.
DeepSeek-V4.1-Flash Multimodal Vision Guide: Image Input to Vision Agent
Multimodal
Intermediate
DeepSeek-V4.1-Flash native vision: compare image URL, Base64, and Files API inputs, with runnable code for OCR, chart reading, and screenshot-driven UI Agent.
Prompt Injection: Indirect Attacks, Tool Poisoning, and Data Exfiltration
AI Safety
Advanced
Once agents read web pages and documents, prompt injection becomes an architecture problem, not a model problem. This article separates direct from indirect injection at the trust boundary, then walks four channels: injection in retrieved content, tool poisoning in MCP descriptions, multimodal injection in images, and exfiltration paths. It ends with a layered defense model.
DeepSeek Model Selection and Deployment Guide
Deployment and Operations
Beginner
Detailed explanation of the DeepSeek full model series: capability characteristics, applicable scenarios, and deployment solutions for models such as V3, R1, Coder, and VL2. A complete guide from model selection to production deployment, including API calls and local deployment tutorials.
RAG Caching and Performance Optimization
RAG Retrieval Augmented
Intermediate
The performance bottleneck of a RAG system is often not the model but the retrieval. This article explains multi-level caching strategies, Redis acceleration, semantic caching, and batch optimization techniques to increase your RAG system's response speed by 10 times.
DeepSeek + Dify Application Development
AI Application Development
Beginner
Quickly Build DeepSeek-Powered AI Applications with the Dify Platform: Chat Assistant, Knowledge Base Q&A, and Agent Workflows. Build production-grade AI applications without writing code through visual orchestration, including complete configuration and deployment tutorials.
DeepSeek Open Source Project Hands-on
AI Application Development
Intermediate
Collection of Practical Open-Source Projects Based on DeepSeek: Intelligent Customer Service System, Code Review Assistant, Document Q&A Bot, AI Search, and Data Analysis Assistant. Each project includes complete code, architecture design, and deployment guide, ready for production use.
Synthetic Data Construction: From Self-Distillation to Adversarial Samples
Model Fine-tuning
Advanced
Model output can't be gold data, and this article starts there. It walks the synthetic data pipeline: the self-distillation and rejection-sampling loop, why distribution collapse is a probability rather than correctness problem, how to quantify the quality-diversity trade-off, curricula and bucketed sampling matched to model ability, and adversarial samples for error injection.
Complete Guide to LoRA Fine-Tuning
Model Fine-tuning
Beginner
LoRA is currently the most popular parameter-efficient fine-tuning method. This article comprehensively explains the complete LoRA fine-tuning process from principles to practice, including environment configuration, dataset preparation, training parameter settings, and model evaluation.
DeepSeek Development Tools Ecosystem
AI Application Development
Intermediate
DeepSeek Development Tool Ecosystem Overview: IDE Plugins, Command-Line Tools, Debugging Tools, Prompt Management, and Evaluation Frameworks. Covers DeepSeek integration solutions and productivity tools for mainstream development environments such as VSCode, JetBrains, and Cursor.
Automated AI Red Teaming: From Manual Adversarial Work to Continuous Evaluation
AI Safety
Advanced
Automated red teaming yields an attack surface map and a regression suite, not one exploit. It maps injection, tool poisoning, multimodal payloads, and privilege abuse; drives mutation and evolutionary search from few seeds to variants under convergence control; automates scoring in three layers: rules, model judges, human review; freezes attacks as CI regression tests.
Complete Guide to Large Model Evaluation and Benchmarking
Model Fine-tuning
Intermediate
How to objectively evaluate the capabilities of large models? This article systematically explains mainstream benchmark tests such as MMLU, HumanEval, and C-Eval, as well as evaluation methods like LLM-as-Judge and arena rankings, to help you establish a scientific model evaluation system.
Large Model Hallucination: Root Cause Analysis and Mitigation Strategies
RAG Retrieval Augmented
Intermediate
Hallucination is one of the most troublesome problems in large model applications. This article provides an in-depth analysis of the causes of hallucination and offers systematic mitigation strategies such as RAG, fact-checking, and uncertainty quantification to help build more reliable AI applications.
Comparison of Local Deployment Options for Large Models
Deployment and Operations
Beginner
Local deployment of large models is a key solution for ensuring data security and controlling costs. This article compares mainstream deployment solutions such as Ollama, vLLM, and llama.cpp, helping you make a choice from dimensions such as ease of use, performance, and hardware requirements.
Install DeepSeek Harness: npx, Global npm & Source Build
Deployment and Operations
Beginner
Install DeepSeek Harness three ways: npx, global npm, or source build. Configure dsh web vs headless, Web UI setup, API Key, and OpenAI-compatible endpoints.
Complete Guide to Local Deployment of DeepSeek Models
Deployment and Operations
Intermediate
Cloud APIs are great, but many scenarios require local deployment—data security, low latency, and offline use. This article comprehensively explains local deployment solutions for DeepSeek models, covering mainstream tools such as Ollama, vLLM, and llama.cpp, from hardware selection to performance tuning.
DeepSeek V4 Thinking Mode: The Complete Guide
DeepSeek API
Intermediate
DeepSeek V4's core innovation—Thinking Mode—allows the model to reason deeply before answering. This article details the thinking.type, reasoning_effort parameters, chain-of-thought extraction, and non-thinking/thinking dual-mode switching strategies, with complete Python/Node.js code examples.
Advanced Multimodal RAG: Hybrid Image-Text Retrieval and Vectorization
Multimodal Development
Intermediate
Documents often contain both text and figures, and pure text-based RAG loses critical information. This article delves into hybrid image-text retrieval: image vectorization, cross-modal alignment, table structured parsing, and fusion re-ranking, to build a RAG system that truly understands images and text.
Graph-Enhanced RAG (GraphRAG) in Practice
RAG Retrieval Augmented
Advanced
Traditional RAG only retrieves text fragments; GraphRAG introduces knowledge graphs on this basis, enabling multi-hop reasoning and relation-aware retrieval. This article explains the construction and practical application of GraphRAG from scratch.
Probing Model Capability Limits: Finding What a Model Really Cannot Do
Model Inference
Advanced
Leaderboard scores and business results diverge. Finding limits: derive the eval set from real failures; measure eight separate ceilings (knowledge, reasoning, instructions, long context, consistency, tool use, structured output, refusal); locate breakpoints with perturbations; calibrate the judge model; map them to routing, validation, externalized state, and human fallback.
引言
Agent Development
Intermediate
2025 年 8 月至 10 月，我在图灵《AI Agent 实战营》中讲授了一系列课程。课程围绕一个核心问题展开：如何以可解释、可验证、可复用的工程原则设计 AI Agent，而不是依赖经验和直觉反复试错？因此，课程不以跑通一个演示系统为终点，而是进一步分析 Agent 为何采用特定架构，以及每项设计决策对应的能力边…
Agent State Machine and Fault-Tolerant Orchestration: Building Reliable Agents
Agent Development
Advanced
In real business, agents fail: tool timeouts, context overflow, loop deadlocks. This article models the agent lifecycle with a finite state machine and explains the complete implementation of fault-tolerant orchestration, including timeout circuit breaking, retry compensation, and session recovery.
MoE Architectures in Practice: Routing, Load Balancing, and Expert Parallelism
Model Architecture
Advanced
MoE is sold as cheaper compute per token, but its real effect is a change in communication topology. What separates dense from sparse activation, top-k softmax gating, the capacity factor and the token-dropping tax, why the load-balancing loss conflicts with the main objective, gradient-free bias balancing, shared experts, and the all-to-all traffic behind expert parallelism.
DeepSeek Harness Python SDK & Multi-Mode Calls: Web UI to Headless
DeepSeek API
Intermediate
Explore DeepSeek Harness beyond the Web UI: headless runs, CLI, Python SDK, and TypeScript SDK. Learn Python SDK sessions, tool callbacks, streaming output, and multimodal input.
LangChain + DeepSeek Complete Integration Guide
AI Application Development
Beginner
LangChain is the most popular framework for AI application development. This article details how to deeply integrate DeepSeek API into the LangChain ecosystem, covering complete usage of ChatModel, Embeddings, Tool Calling, Chain, and Agent.
Practical Construction of Instruction Fine-Tuning Data
Model Fine-tuning
Intermediate
Data quality determines the upper limit of fine-tuning effectiveness. This article explains the construction methods for instruction fine-tuning data, including data sources, cleaning strategies, quality evaluation, data augmentation, and mixing design.
Scope Isolation & Event System in DeepSeek Harness Plugins
Agent Development
Advanced
Learn dsh scope semantics and the event bus: auto-isolated registrations, cross-plugin communication via services or events, naming and payload conventions, listener leak fixes.
DeepSeek × Claude Code Integration in Practice
DeepSeek API
Beginner
DeepSeek has been integrated into mainstream agent tools such as Claude Code, GitHub Copilot, and OpenCode. This article provides a hands-on guide to using DeepSeek as the backend model to drive Claude Code, enjoying a top-tier coding experience with extremely low API costs.
