acaprino/rag-development
plugins/rag-development/skills/rag-development/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add acaprino/anvil-toolset rag-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- rag-development
- description:
- >
- 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.
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:
- Chunking: Recursive character splitting at 512 tokens, 10-15% overlap
- Embedding: OpenAI
text-embedding-3-small(best value) or Cohereembed-v4(best accuracy) - Vector DB: Qdrant with scalar INT8 quantization
- Retrieval: Hybrid search (dense + sparse + RRF)
- 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 guideembedding-models.md-- model comparison, Matryoshka embeddings, fine-tuning, sparse/dense/multi-vectorretrieval-patterns.md-- hybrid search, HyDE, contextual retrieval, re-ranking, MMRadvanced-rag-patterns.md-- Graph RAG, RAPTOR, CRAG, Self-RAG, Agentic RAG, multi-modal RAGvector-databases.md-- Qdrant deep dive, database comparison, scaling strategiesproduction-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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