AI 开发 教程
系统化的 AI 开发学习路径。从提示词工程到模型微调，从 RAG 到 Agent 开发，覆盖 AI 应用开发全栈知识。共 142 篇教程，18 个分类，入门到高级全覆盖。
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DeepSeek Model Evaluation and Benchmarking
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.
阅读教程 →DeepSeek RAG Retrieval-Augmented Tutorial
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.
阅读教程 →DeepSeek Prompt Engineering Tutorial
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.
阅读教程 →DeepSeek Agent Development Tutorial
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.
阅读教程 →DeepSeek Multimodal Application Tutorial
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.
阅读教程 →DeepSeek Deployment and Operations Tutorial
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.
阅读教程 →DeepSeek + LangChain Development Tutorial
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.
阅读教程 →DeepSeek Open Source Project Hands-on
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.
阅读教程 →DeepSeek Development Tools Ecosystem
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.
阅读教程 →DeepSeek-Flash API Migration: From V4 Pro, Cost-Optimized
V4 Pro retires Sept 14. Migrate smoothly to deepseek-flash with compatible routing, OpenAI/Anthropic formats, Thinking mode, vision, Tool Calls, and cost optimization.
阅读教程 →DeepSeek-V4.1-Flash Multimodal Vision Guide: Image Input to Vision Agent
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.
阅读教程 →DeepSeek-V4.1-Flash Benchmarks & Model Selection Guide
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.
阅读教程 →DeepSeek V4 Thinking Mode: The Complete Guide
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.
阅读教程 →FIM Code Completion: Making DeepSeek Your AI Programming Assistant
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.
阅读教程 →Streaming Output in Practice: SSE Handling and Frontend Rendering
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.
阅读教程 →Tool Calls in Practice: The Complete Guide to DeepSeek V4 Function Calling
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.
阅读教程 →JSON Mode Structured Output: From Schema to Production
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.
阅读教程 →Efficient Use of 1M Context: Strategies for Multi-Turn Dialogues and Long Document Processing
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.
阅读教程 →DeepSeek V4 Flash Performance In-Depth Review
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.
阅读教程 →DeepSeek Multimodal Development in Practice: Janus-Pro Image Understanding and Generation
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.
阅读教程 →AI Application Security and Content Moderation Best Practices
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.
阅读教程 →AI Application Observability: Logging, Monitoring, and Alerting in Practice
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.
阅读教程 →Prompt Compression and Semantic Caching Engineering in Practice
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.
阅读教程 →RAG Re-ranking and Hybrid Search in Practice: From BM25 to Cross-Encoder
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.
阅读教程 →Long Context in Practice: KV Cache and 1M Token Application Design
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.
阅读教程 →AI Workflow Engine in Practice: From Scripts to Visual Orchestration
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.
阅读教程 →RAG Agent Application Implementation: From Knowledge Base to Autonomous Decision-Making
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.
阅读教程 →MCP Server Development in Practice: From Protocol to Production-Grade Tool Service
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.
阅读教程 →Test-Driven Development for LLM Applications: Practical Prompt Regression Testing
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.
阅读教程 →Advanced Multimodal RAG: Hybrid Image-Text Retrieval and Vectorization
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.
阅读教程 →Advanced Prompt Engineering: Structured Prompts, CoT, Self-Reflection
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.
阅读教程 →RAG Performance Optimization: From Retrieval Accuracy to End-to-End Latency
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.
阅读教程 →Tool Calling Loop: Enabling AI to Use External Tools
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.
阅读教程 →Reflection and Self-Correction: Doubling AI Output Quality
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.
阅读教程 →DeepSeek API in Action: From Getting Started to Production Deployment
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.
阅读教程 →Structured Prompt Design Patterns
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.
阅读教程 →Multi-turn Dialogue Prompt Strategies
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.
阅读教程 →Vector Database Selection in Practice
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.
阅读教程 →Deep Optimization of Document Chunking Strategies
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.
阅读教程 →Building an AI Code Review System
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.
阅读教程 →Building a Smart Customer Service Agent from Scratch
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.
阅读教程 →Voice AI Application Development in Practice
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.
阅读教程 →Complete Guide to Local Deployment of DeepSeek Models
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.
阅读教程 →AI API Cost Optimization in Practice
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%.
阅读教程 →Practical Guide to QLoRA Fine-Tuning
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.
阅读教程 →The Art of Building Fine-Tuning Datasets
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.
阅读教程 →Agent Security and Permission Control
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.
阅读教程 →Agent Testing and Debugging Strategies
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.
阅读教程 →Comprehensive Comparison of LoRA vs QLoRA vs Full Fine-tuning
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.
阅读教程 →Practical Construction of Instruction Fine-Tuning Data
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.
阅读教程 →Practical Model Quantization and Deployment
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.
阅读教程 →Design of Multilingual RAG Systems
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.
阅读教程 →RAG Caching and Performance Optimization
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.
阅读教程 →Best Practices for Containerizing AI Services
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.
阅读教程 →AI Testing in CI/CD Pipelines
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.
阅读教程 →AI-Driven Automated Testing Systems
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.
阅读教程 →Prompt Version Management and A/B Testing
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.
阅读教程 →Best Practices for System Prompt Engineering
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.
阅读教程 →Complete Guide to Large Model Evaluation and Benchmarking
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.
阅读教程 →Multimodal AI Application Development in Practice
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.
阅读教程 →Large Model Hallucination: Root Cause Analysis and Mitigation Strategies
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.
阅读教程 →AI Agent Tool Development in Practice: From Design to Deployment
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.
阅读教程 →Panorama of Large Model Inference Acceleration Technologies
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.
阅读教程 →QLoRA Quantized Fine-Tuning in Practice
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.
阅读教程 →High-Performance Inference Deployment with vLLM
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.
阅读教程 →Chain-of-Thought (CoT) and Few-shot Prompting Techniques
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.
阅读教程 →Vector Database Selection: Chroma vs Pinecone vs Milvus
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.
阅读教程 →AI Agent Architecture Design: From ReAct to Plan-and-Execute
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.
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