RAG Implementation Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. When to Use This Skill - 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 Core Components 1. Vector Databases Purpose : Store and retri…