RAG Bootcamp: Legal Document Q&A System
Master Retrieval-Augmented Generation by building a legal document Q&A system from scratch. Across 14 video lessons with bilingual content (English + 中文), you'll ingest legal contracts, chunk and embed them, and deploy a production RAG pipeline that answers natural-language questions about your documents.
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Lesson Overview
Lesson 1: What is RAG?
Understand the problem RAG solves, the library analogy, RAG vs fine-tuning, and real-world use cases.
Lesson 2: Embeddings & Vector Similarity
Learn what embeddings are, how similarity works, cosine similarity, and embedding dimensions.
Lesson 3: Vector Databases & Chunking
Understand vector database types (Pinecone, ChromaDB, Weaviate), chunking strategies, and overlap.
Lesson 4: RAG Pipeline Architecture
End-to-end pipeline: indexing, retrieval, generation. Tech stack comparison of free vs industry stacks.
Lesson 5: Environment Setup
Set up your development environment with the free stack (Ollama + ChromaDB) or industry stack (OpenAI + Pinecone).
Lesson 6: Document Ingestion
Process PDF, DOCX, and TXT files with recursive chunking and metadata extraction.
Lesson 7: Building Retrieval
Embed documents, store in a vector DB, and implement similarity search queries.
Lesson 8: LLM Integration
Prompt engineering for RAG, context injection, streaming responses, and anti-hallucination prompts.
Lesson 9: Building the Web UI
Build a Gradio web interface with source citations and conversation memory.
Lesson 10: Re-ranking & Hybrid Search
Two-stage retrieval with cross-encoder re-ranking, BM25 keyword search combined with dense vector search.
Lesson 11: RAG Evaluation & Testing
Retrieval metrics (Recall@K, Precision@K, MRR, NDCG), generation metrics (Faithfulness, Relevance, Hallucination Detection), RAGAS framework, and automated testing pipelines.
Lesson 12: Deployment & Production
Containerize your RAG app with Docker, build FastAPI endpoints, add semantic caching, and deploy to Railway or Render.
Lesson 13: Monitoring & Continuous Improvement
Structured logging, alerting rules, diagnostic decision trees, and continuous improvement loops for production RAG systems.
Lesson 14: RAG Interview Q&A
Top concept, architecture, implementation, and system design interview questions with detailed answers.