Agent skill · research science · aiskillstore
rag-engineer
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.
Why this skill is useful
Provides domain-specific knowledge on retrieval-augmented generation systems, including chunking strategies and hybrid search techniques that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Hierarchical Retrieval Multi-level retrieval for better precision Hybrid Search Combine semantic and keyword search 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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/rag-engineer