rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
npx skills add majiayu000/claude-skill-registry --skill rag-implementation-liuchiawei-agent-skills --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 Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. ## Use this skill when - Building Q&A systems over proprietary documents - Creating chatbots with current, factual information - Implementing semantic search with natural language queries - Reducing hallucinations with grounded responses - Enabling LLMs to access domain-specific knowledge - Building documentation assistants - Creating research tools with source citation ## Do not use this skill when - You only need purely generative writing without retrieval - The dataset is too small to justify embeddings - You cannot store or process the source data safely ## Instructions 1. Define the corpus, update cadence, and evaluation targets. 2. Choose embedding models and vector store based on scale. 3. Build ingestion, chunking, and retrieval with reranking. 4. Evaluate with grounded QA metrics and monitor drift. ## Safety - Redact sensitive data and enforce access controls. - Avoid exposing source documents in responses when restricted. ## Core Components ### 1. Vector Databases **Purpose**: Store and retrieve document emb
- Use this skill when
- Do not use this skill when
- Instructions
- Safety
- Core Components
- 1. Vector Databases
- 2. Embeddings
- 3. Retrieval Strategies
- 4. Reranking
- Quick Start
- Advanced RAG Patterns
- Pattern 1: Hybrid Search
- Pattern 2: Multi-Query Retrieval
- Pattern 3: Contextual Compression
What does the rag-implementation skill do?
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-implementation-liuchiawei-agent-skills --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.