ai rag recommended
RAG Patterns
Retrieval Augmented Generation patterns covering chunking strategies, embeddings, vector stores, retrieval optimization, and production best practices.
- Difficulty
- advanced
- Read time
- 1 min read
- Version
- v1.0.0
- Confidence
- established
- Last updated
Quick Reference
RAG: Chunking matters more than embedding model. Small chunks (256 tokens) for retrieval precision, expand context before generation. Overlap chunks 10-20%. Use semantic chunking for unstructured docs. Hybrid search (vector + keyword) outperforms pure vector. Re-rank retrieved results. Test with domain-specific queries.
Use When
- Document Q&A systems
- Knowledge bases
- Chatbots with context
- Search applications
- Customer support
Skip When
- Simple prompt engineering sufficient
- Real-time data required
- No relevant documents exist
RAG Patterns
Retrieval Augmented Generation patterns covering chunking strategies, embeddings, vector stores, retrieval optimization, and production best practices.