Phase 11: LLM 工程

本阶段包含 17 课时。

原始课程来源：AI Engineering from Scratch (MIT License)

---

• Prompt Engineering: Techniques & Patterns
• Few-Shot, Chain-of-Thought, Tree-of-Thought
• Structured Outputs: JSON, Schema Validation, Constrained Decoding
• Embeddings & Vector Representations
• Context Engineering: Windows, Budgets, Memory, and Retrieval
• RAG (Retrieval-Augmented Generation)
• Advanced RAG (Chunking, Reranking, Hybrid Search)
• Fine-Tuning with LoRA & QLoRA
• Function Calling & Tool Use
• Evaluation & Testing LLM Applications
• Caching, Rate Limiting & Cost Optimization
• Guardrails, Safety & Content Filtering
• Building a Production LLM Application
• Model Context Protocol (MCP)
• Prompt Caching and Context Caching
• LangGraph — State Machines for Agents
• Agent Framework Tradeoffs — LangGraph vs CrewAI vs AutoGen vs Agno