RAG (Retrieval-Augmented Generation)
💡 Quick Definition: An AI framework that dynamically retrieves private documents from a vector database to provide accurate, grounded context to Large Language Models.
Detailed Explanation & Workplace Application
RAG prevents LLM hallucinations by retrieving factual chunks of text from internal knowledge bases (PDFs, SQL databases, customer tickets) and injecting them into the prompt before generating the final answer. It eliminates the high cost and latency of retraining or fine-tuning models.
Practical Syntax / Framework Formula
// Standard RAG Workflow: 1. User Query -> Embedding Model -> Vector DB similarity search 2. Top 3 matching document chunks retrieved 3. LLM Prompt: "Answer query based ONLY on Context: [Chunks] User: [Query]"