rag-implementation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. Use when building RAG systems.
npx skills add majiayu000/claude-skill-registry --skill rag-implementation-bcastelino-agent-skills-kit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RAG Implementation Workflow ## Overview Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation. ## When to Use This Workflow Use this workflow when: - Building RAG-powered applications - Implementing semantic search - Creating knowledge-grounded AI - Setting up document Q&A systems - Optimizing retrieval quality ## Workflow Phases ### Phase 1: Requirements Analysis #### Skills to Invoke - `ai-product` - AI product design - `rag-engineer` - RAG engineering #### Actions 1. Define use case 2. Identify data sources 3. Set accuracy requirements 4. Determine latency targets 5. Plan evaluation metrics #### Copy-Paste Prompts ``` Use @ai-product to define RAG application requirements ``` ### Phase 2: Embedding Selection #### Skills to Invoke - `embedding-strategies` - Embedding selection - `rag-engineer` - RAG patterns #### Actions 1. Evaluate embedding models 2. Test domain relevance 3. Measure embedding quality 4. Consider cost/latency 5. Select model #### Copy-Paste Prompts ``` Use @embedding-strategies to select optimal embedding model ``
- Overview
- When to Use This Workflow
- Workflow Phases
- Phase 1: Requirements Analysis
- Phase 2: Embedding Selection
- Phase 3: Vector Database Setup
- Phase 4: Chunking Strategy
- Phase 5: Retrieval Implementation
- Phase 6: LLM Integration
- Phase 7: Caching
- Phase 8: Evaluation
- RAG Architecture
- Quality Gates
- Related Workflow Bundles
What does the rag-implementation skill do?
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. Use when building RAG systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-implementation-bcastelino-agent-skills-kit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.