rag-implementation
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
- Installs
- 496
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/davila7/claude-code-templates --skill rag-implementation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads rag-implementation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
RAG Implementation
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
FAQ
What does the rag-implementation skill do?
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
How do I install rag-implementation?
Run npx skills add https://github.com/davila7/claude-code-templates --skill rag-implementation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-implementation/ (personal) or .claude/skills/rag-implementation/ (project).
Which AI agents can use rag-implementation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is rag-implementation safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains rag-implementation?
It is published by davila7 in davila7/claude-code-templates. It has 496 recorded installs.
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