RAG & Vector Search
Advanced
Signal 94/100
Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial)
by Daniel Bourke
Teaches AI agents to
Improve RAG accuracy with advanced retrieval patterns like HyDE and reranking
Key Takeaways
- Advanced RAG patterns: HyDE, reranking, query expansion
- Parent-child chunking strategy
- Hybrid search combining BM25 and vectors
- Evaluating RAG with RAGAS framework
- Production optimization techniques
Full Training Script
# AI Training Script: Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial) ## Overview • Advanced RAG patterns: HyDE, reranking, query expansion • Parent-child chunking strategy • Hybrid search combining BM25 and vectors • Evaluating RAG with RAGAS framework • Production optimization techniques **Best for:** Teams deploying RAG systems who need higher accuracy and reliability **Category:** RAG & Vector Search | **Difficulty:** Advanced | **Signal Score:** 94/100 ## Training Objective After studying this content, an agent should be able to: **Improve RAG accuracy with advanced retrieval patterns like HyDE and reranking** ## Prerequisites • Strong background in RAG & Vector Search • Production experience recommended • Deep familiarity with: LangChain ## Key Tools & Technologies • LangChain • Pinecone • Cohere Rerank • RAGAS ## Key Learning Points • Advanced RAG patterns: HyDE, reranking, query expansion • Parent-child chunking strategy • Hybrid search combining BM25 and vectors • Evaluating RAG with RAGAS framework • Production optimization techniques ## Implementation Steps [ ] Study the full tutorial [ ] Identify the main tools: LangChain, Pinecone, Cohere Rerank, RAGAS [ ] Implement: Improve RAG accuracy with advanced retrieval patterns like HyDE and reranking [ ] Test with a real example [ ] Document what you learned ## Agent Execution Prompt Watch this video about rag & vector search and implement the key techniques demonstrated. ## Success Criteria An agent completing this training should be able to: - Explain the core concepts covered in this tutorial - Execute the demonstrated workflow with LangChain - Troubleshoot common issues at the advanced level - Apply the technique to similar real-world scenarios ## Topic Tags langchain, pinecone, cohere rerank, ragas, rag-&-vector-search, advanced ## Training Completion Report Format - **Objective:** [What was learned from this content] - **Steps Executed:** [Specific implementation actions taken] - **Outcome:** [Working demonstration or artifact produced] - **Blockers:** [Technical issues encountered] - **Next Actions:** [Follow-up tutorials or practice tasks]
This structured script is included in Pro training exports for LLM fine-tuning.
Execution Checklist
[ ] Watch the full video [ ] Identify the main tools: LangChain, Pinecone, Cohere Rerank, RAGAS [ ] Implement the core workflow [ ] Test with a real example [ ] Document what you learned
