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。
找到 32 篇教程
DeepSeek Safety and Content Moderation
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.
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 Enterprise Application Tutorial
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.
阅读教程 →DeepSeek API Development Documentation
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.
阅读教程 →DeepSeek Getting Started Guide
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.
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 + Dify Application Development
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.
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 Model Introduction
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.
阅读教程 →LangChain + DeepSeek Complete Integration Guide
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.
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 →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 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 →DeepSeek Open Source Project Selection: Top 10 Tools Worth Following on GitHub
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.
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 →DeepSeek-V3 vs R1: Complete Model Selection Guide (2026 Latest)
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.
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 →AI-Driven Data Analysis Pipeline
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.
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 →Design of AI Writing Assistant Tools
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.
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 →Enterprise AI Middleware Architecture Design
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.
阅读教程 →Getting Started with Low-Code AI Application Development
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.
阅读教程 →DeepSeek API Quick Start: From Registration to First Application
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.
阅读教程 →Large Model Safety and Alignment Techniques
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.
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 →AI Application Performance Optimization and Cost Control
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.
阅读教程 →Building AI Applications with Dify Low-Code Platform
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.
阅读教程 →LangChain Basics: Building Your First AI Application
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.
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