davila7 / rag-engineer
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mkdir -p .claude/skills/rag-engineer && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/1659" && unzip -o skill.zip -d .claude/skills/rag-engineer && rm skill.zipProject Skills
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
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--- name: rag-engineer description: "Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval." source: vibeship-spawner-skills (Apache 2.0) --- # RAG Engineer **Role**: RAG Systems Architect I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out. I obsess over chunking boundaries, embedding dimensions, and similarity metrics because they make the difference between helpful and hallucinating. ## Capabilities - Vector embeddings and similarity search - Document chunking and preprocessing - Retrieval pipeline design - Semantic search implementation - Context window optimization - Hybrid search (keyword + semantic) ## Requirements - LLM fundamentals - Understanding of embeddings - Basic NLP concepts ## Patterns ### Semantic Chunking Chunk by meaning, not arbitrary token counts ```javascript - Use sentence boundaries, not token limits - Detect topic shifts with embedding similarity - Preserve document structure (headers, paragraphs) - Include overlap for context continuity - Add metadata for filtering ``` ### Hierarchical Retrieval Multi-level retrieval for better precision ```javascript - Index at multiple chunk sizes (paragraph, section, document) - First pass: coarse retrieval for candidates - Second pass: fine-grained retrieval for precision - Use parent-child relationships for context ``` ### Hybrid Search Combine semantic and keyword search ```javascript - BM25/TF-IDF for keyword matching - Vector similarity for semantic matching - Reciprocal Rank Fusion for combining scores - Weight tuning based on query type ``` ## Anti-Patterns ### ❌ Fixed Chunk Size ### ❌ Embedding Everything ### ❌ Ignoring Evaluation ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: | | Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: | | Using same embedding model for different content types | medium | Evaluate embeddings per content type: | | Using first-stage retrieval results directly | medium | Add reranking step: | | Cramming maximum context into LLM prompt | medium | Use relevance thresholds: | | Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: | | Not updating embeddings when source documents change | medium | Implement embedding refresh: | | Same retrieval strategy for all query types | medium | Implement hybrid search: | ## Related Skills Works well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`