By version: 4.1.0-fractal 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.
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
- Define the corpus, update cadence, and evaluation targets.
- Choose embedding models and vector store based on scale.
- Build ingestion, chunking, and retrieval with reranking.
- Evaluate with grounded QA metrics and monitor drift.
Safety
- Redact sensitive data and enforce access controls.
- Avoid exposing source documents in responses when restricted.