rag-implementation
Builds retrieval-augmented generation systems with vector databases and semantic search.
- Kind
- Skill
- Works with
- Claude Code, Codex
- Source
- wshobson/agents @ 0874a27b17a2
- Path
- plugins/llm-application-dev/skills/rag-implementation/SKILL.md
- License
- MIT
- Jobs
- Implementation
InstallInstall it from a plugin marketplace.
/plugin install llm-application-dev@claude-code-workflows
# RAG Implementation Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. ## When to Use This Skill - 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 ## Core Components ### 1. Vector Databases **Purpose**: Store and retrieve document embeddings efficiently **Options:** - **Pinecone**: Managed, scalable, serverless - **Weaviate**: Open-source, hybrid search, GraphQL - **Milvus**: High performance, on-premise - **Chroma**: Lightweight, easy to use, local development - **Qdrant**: Fast, filtered search, Rust-based - **pgvector**: PostgreSQL extension, SQL integration ### 2. Embeddings **Purpose**: Convert text to numerical vectors for similarity search **Models (2026):** | Model | Dimensions | Best For | |-------|------------|----------| …