RAG Implementation

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.

主要功能

使用场景