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.
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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.
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</> SKILL.md
rag-implementation.SKILL.md
---name: rag-implementation
description: 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.
---# 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
