RAG Implementation Guide
PROBuild Retrieval-Augmented Generation systems that ground LLM responses in external knowledge sources. Reduce hallucinations and enable domain-specific AI.
Build production-ready RAG (Retrieval-Augmented Generation) systems. Chunking strategies, embedding selection, and retrieval optimization.
Example Usage
Build a RAG system for my company’s internal documentation. I have 500 PDF manuals and want employees to ask questions in natural language.
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| Vector database to use | Chroma | |
| Embedding model | OpenAI | |
| Programming language I'm using | Python |
What You’ll Get
- Architecture design
- Component selection recommendations
- Implementation code
- Optimization strategies
Research Sources
This skill was built using research from these authoritative sources:
- Anthropic: RAG with Claude Official Claude RAG implementation guide
- LangChain: RAG Tutorial Comprehensive RAG implementation with LangChain
- Pinecone: RAG Guide Vector database RAG patterns and best practices
- OpenAI: Embeddings Guide Text embeddings for semantic search
- Llamaindex: RAG Documentation Advanced RAG patterns and data connectors