LLM 应用开发：从 Prompt Engineering 到 RAG
LLM Application Development — From Prompt Engineering to RAG
| iDev Team | 2026-08-12T01:35:00
大语言模型（LLM）怎么从「玩具」变成「工具」？系统讲解 Prompt Engineering、Function Calling 和 RAG 三大核心技术。
How to turn LLMs from 'toys' into 'tools'? A systematic guide covering Prompt Engineering, Function Calling, and RAG — the three core techniques.
Prompt Engineering：让 AI 听话Prompt 不是随便写一句话。好的 Prompt 包含：角色设定、任务描述、输出格式要求、示例（Few-shot）。掌握这些技巧，AI 输出质量提升 3 倍以上。Function Calling：让 AI 做事LLM 本身只能生成文本，但通过 Function Calling，你可以让它调用你的 API：查数据库、发邮件、操作系统。AI 从「聊天机器人」变成「智能助手」。RAG：让 AI 靠谱纯 LLM 会「幻觉」——编造不存在的信息。RAG（检索增强生成）先从你的知识库检索相关内容，再让 LLM 基于这些内容生成回答。回答准确率从 60% 提升到 95%+。技术栈推荐LLM：Claude API / OpenAI API（按需选择）向量数据库：Milvus 或 Pinecone框架：LangChain（Python）或直接调 API（更可控）后端：Spring Boot 或 FastAPI落地建议不要试图一步做一个「万能 AI」。先找一个具体场景（客服、文档问答、数据分析），做一个 MVP 验证效果，再逐步扩展。
Prompt Engineering: Making AI Follow InstructionsPrompts aren't just random sentences. Good prompts include: role definition, task description, output format requirements, and examples (few-shot). Master these techniques and AI output quality improves 3x+.Function Calling: Making AI Take ActionLLMs can only generate text by default, but with Function Calling, you can have them invoke your APIs: query databases, send emails, operate systems. AI transforms from "chatbot" to "intelligent assistant."RAG: Making AI ReliablePure LLMs hallucinate — fabricating nonexistent information. RAG (Retrieval-Augmented Generation) first retrieves relevant content from your knowledge base, then has the LLM generate answers based on that content. Answer accuracy jumps from 60% to 95%+.Recommended Tech StackLLM: Claude API / OpenAI API (choose as needed)Vector DB: Milvus or PineconeFramework: LangChain (Python) or direct API calls (more control)Backend: Spring Boot or FastAPIImplementation AdviceDon't try to build an "omniscient AI" in one go. Start with one specific scenario (customer service, document Q&A, data analysis), build an MVP to validate results, then gradually expand.