Advanced14 min
Project: Build a RAG Q&A System
End-to-end walkthrough: build a production RAG system with ingestion pipeline, hybrid search, self-corrective retrieval, answer validation, and continuous evaluation.
Quick Reference
- →Ingestion: document loaders → semantic chunking → embedding → Pinecone with metadata
- →Retrieval: hybrid search (semantic + keyword) → reranking → document grading
- →Self-corrective: if documents fail grading, rewrite query and re-retrieve (max 2 retries)
- →Generation: context-grounded answer with citation extraction
- →Answer validation: check for hallucinated claims not supported by source docs
- →Evaluation: faithfulness, relevance, and answer quality via LangSmith online eval
Architecture Overview
| Phase | Components | Purpose |
|---|---|---|
| Ingestion | Loaders → Chunker → Embedder → Vector Store | Build the knowledge base |
| Retrieval | Hybrid search → Reranker → Document grader | Find relevant context |
| Self-correction | Query rewriter → Re-retrieve → Re-grade | Fix bad retrievals |
| Generation | LLM with grounded context → Citation extractor | Answer with sources |
| Validation | Hallucination checker → Faithfulness scorer | Ensure answer quality |
| Evaluation | Online eval → Annotation queues → Feedback loops | Monitor and improve |