RAG Implementation

About

Master the implementation of Retrieval-Augmented Generation (RAG) to ground LLM responses in external, domain-specific knowledge. This skill provides a comprehensive framework for selecting vector databases, choosing embedding models, and implementing advanced retrieval patterns like hybrid search, multi-querying, and contextual compression. It covers essential document processing techniques including recursive chunking and semantic splitting, while offering guidance on prompt engineering for source citations and evaluation metrics to ensure accurate, hallucination-free AI applications.

Key Features

• Sophisticated document chunking methods including semantic, token-based, and markdown-header splitting.
• Advanced retrieval patterns such as hybrid search, multi-query variations, and contextual compression.
• Prompt engineering templates for building grounded Q&A systems with verifiable citations.
• Implementation guidance for vector databases including Pinecone, Chroma, Weaviate, and FAISS.
• 23,139 GitHub stars
• Reranking strategies using cross-encoders to improve top-k result relevance.

Use Cases

• Integrating LLMs with external knowledge bases to reduce hallucinations and ensure factual accuracy.
• Building proprietary document Q&A systems and internal research tools.
• Creating documentation assistants that provide responses grounded in specific technical manuals.