rag-architectlisted

# RAG Architect Design retrieval-augmented generation pipelines with the right tradeoffs at each layer. ## Workflow 1. **Choose chunking strategy** -- Match chunk method to document structure 2. **Select embedding model** -- Balance dimensions, speed, and domain fit 3. **Choose vector DB** -- Match scale, features, and deployment model 4. **Design retrieval** -- Dense, sparse, hybrid, or reranked 5. **Evaluate** -- Measure faithfulness, relevance, and answer quality ## Reading Guide | Decision | File | | ---------------------------------------------- | ------------------------------------------------------------ | | Chunking strategies + embedding models | [chunking-and-embedding.md](./chunking-and-embedding.md) | | Retrieval strategies + vector DBs + evaluation | [retrieval-and-evaluation.md](./retrieval-and-evaluation.md) | ## Quick Decision Matrix | Document type | Chunking | Embedding | Retrieval | | -------------- | ------------------------- | ---------------- | --------------- | | Code | Semantic (AST-aware) | Code-specialized | Hybrid + rerank | | Legal/medical | Document-aware (sections) | Domain-specific | Dense + rerank | | Chat logs | Sentence | General-purpose | Dense | | Technical docs | Recursive | General-purpose | Hybrid | | Mixed/unknown | R