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 28 篇教程
DeepSeek Model Selection and Deployment Guide
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.
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 Model Download and Installation
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.
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 + 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 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 →DeepSeek API Cost Optimization in Practice: Cache Hits Save 98%
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.
阅读教程 →Zero-Code Migration: Replacing Claude API with DeepSeek
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.
阅读教程 →DeepSeek × Claude Code Integration in Practice
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.
阅读教程 →Token Usage Precision and Rate Limiting Strategies
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.
阅读教程 →DeepSeek API from Zero to Launch: A Complete Project Walkthrough
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.
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 →Introduction to the MCP Protocol: Building Standard Interfaces for AI Tool Calls
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.
阅读教程 →Agent Loop Basics: Understanding AI Autonomous Workflows from Scratch
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.
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-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 →Getting Started with Multimodal RAG
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.
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 →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 →Building an Enterprise-Grade AI Knowledge Base System from Scratch
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.
阅读教程 →Complete Guide to LoRA Fine-Tuning
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.
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 →Prompt Engineering Basics: From Zero to Mastery
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.
阅读教程 →RAG from Beginner to Production: Complete Tech Stack
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.
阅读教程 →Function Calling in Practice: Let AI Invoke External Tools
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.
阅读教程 →Comparison of Local Deployment Options for Large Models
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.
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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