RAG Implementation Guide

About

This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) within LLM applications. It guides developers through the entire pipeline, including document ingestion, semantic chunking, vector database integration, and advanced retrieval strategies like hybrid search and reranking. By following these patterns, developers can create AI systems that provide factually accurate, source-cited responses while significantly reducing hallucinations and improving context-aware performance across proprietary datasets.

Key Features

• Advanced vector database integration (Pinecone, Weaviate, Chroma, FAISS)
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• Contextual compression and reranking for optimized search results
• Comprehensive RAG evaluation metrics for accuracy and groundedness
• Sophisticated document chunking and semantic splitting strategies
• Implementation of hybrid search and multi-query retrieval patterns

Use Cases

• Implementing semantic search for large-scale enterprise documentation
• Building proprietary document Q&A systems and research tools
• Creating factual chatbots with real-time access to external knowledge