rag-development
Comprehensive RAG development knowledge base covering chunking, embeddings, vector databases, retrieval strategies, advanced patterns (Graph RAG, CRAG, Self-RAG, Agentic RAG), evaluation, and production deployment. TRIGGER WHEN: building, implementing, writing, coding, creating, optimizing, or auditing RAG systems. DO NOT TRIGGER WHEN: the task is outside the specific scope of this component.
What this skill does
# RAG Development Comprehensive knowledge base for building production-grade Retrieval-Augmented Generation systems. ## When to Use - Building a new RAG pipeline from scratch - Choosing chunking strategy, embedding model, or vector database - Implementing hybrid search, re-ranking, or contextual retrieval - Evaluating RAG quality with RAGAS or DeepEval - Optimizing production RAG for cost, latency, or accuracy - Designing multi-tenant RAG with access control - Upgrading from naive RAG to advanced patterns ## Quick Start Recommendation For 80% of use cases, start with: 1. **Chunking**: Recursive character splitting at 512 tokens, 10-15% overlap 2. **Embedding**: OpenAI `text-embedding-3-small` (best value) or Cohere `embed-v4` (best accuracy) 3. **Vector DB**: Qdrant with scalar INT8 quantization 4. **Retrieval**: Hybrid search (dense + sparse + RRF) 5. **Evaluation**: RAGAS metrics from day one Then upgrade incrementally based on measured failures: - Keyword misses -> add sparse vectors (SPLADE/BM25) - Ambiguous chunks -> add contextual retrieval (Anthropic pattern) - Irrelevant results -> add cross-encoder re-ranking - Multi-hop failures -> upgrade to agentic RAG ## Reference Materials Detailed reference documents are in the `references/` directory: - `chunking-strategies.md` -- all chunking approaches with code, benchmarks, and selection guide - `embedding-models.md` -- model comparison, Matryoshka embeddings, fine-tuning, sparse/dense/multi-vector - `retrieval-patterns.md` -- hybrid search, HyDE, contextual retrieval, re-ranking, MMR - `advanced-rag-patterns.md` -- Graph RAG, RAPTOR, CRAG, Self-RAG, Agentic RAG, multi-modal RAG - `vector-databases.md` -- Qdrant deep dive, database comparison, scaling strategies - `production-guide.md` -- evaluation, observability, caching, security, cost optimization ## Pipeline Architecture ``` Document Ingestion: Raw Docs -> Preprocessing (Unstructured.io) -> Chunking -> Context Enrichment -> Embedding -> Vector DB Query Pipeline: User Query -> Query Transform -> Encode (Dense + Sparse) -> Hybrid Search -> Re-rank -> LLM Generation Evaluation Loop: Ground Truth + Predictions -> RAGAS/DeepEval -> Faithfulness, Relevancy, Precision, Recall ``` ## Key Decision Points | Decision | Default | Upgrade When | |----------|---------|-------------| | Chunking | Recursive 512 tok | Structured docs -> markdown-aware; cross-refs -> late chunking | | Embedding | text-embedding-3-small | Need accuracy -> embed-v4; self-hosted -> NV-Embed-v2 | | Vector DB | Qdrant + INT8 | Already on Postgres -> pgvector; need managed -> Pinecone | | Search | Dense only | Keyword misses -> add sparse hybrid; poor diversity -> add MMR | | Re-ranking | None | Top-k results contain irrelevant items -> add Cohere Rerank | | Caching | None | Production latency/cost concerns -> semantic cache | | Evaluation | Manual spot checks | Any production use -> RAGAS automated metrics |
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