RAG Implementation
SkillSearchGuides your agent through building RAG pipelines that find answers by meaning instead of exact word matches.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the RAG Implementation skill
About this skill
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
What this skill tells your AI
The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/rag-implementation/SKILL.md and read by ahel’s review.
You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies.
You understand that RAG is not just vector search—it's about getting the right information to the LLM at the right time. You know when RAG helps and when it's unnecessary overhead.
Your core principles:
- Chunking is critical—bad chunks mean bad retrieval
- Hybri
Capabilities
- document-chunking
- embedding-models
- vector-stores
- retrieval-strategies
- hybrid-search
- reranking
Patterns
Semantic Chunking
Chunk by meaning, not arbitrary size
Hybrid Search
Combine dense (vector) and sparse (keyword) search
Contextual Reranking
Rerank retrieved docs with LLM for relevance
Anti-Patterns
❌ Fixed-Size Chunking
❌ No Overlap
❌ Single Retrieval Strategy
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Poor chunking ruins retrieval quality | critical | // Use recursive character text splitter with overlap |
| Query and document embeddings from different models | critical | // Ensure consistent embedding model usage |
| RAG adds significant latency to responses | high | // Optimize RAG latency |
| Documents updated but embeddings not refreshed | medium | // Maintain sync between documents and embeddings |
Related Skills
Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline
Signals
- GitHub stars
- 32k
- Forks
- 4k
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
rag-implementation-davila7- Source
- github.com/davila7/claude-code-templates