RAG Simplified: Enhance AI Accuracy with Real-Time Retrieval

Explore how RAG enhances AI assistants by grounding answers with real data and improving accuracy.

RAG, or Retrieval-Augmented Generation, is a technique that combines the power of large language models (LLMs) with external knowledge sources to generate more accurate, grounded, and contextually rich responses. In simple terms, RAG means that when the model receives a prompt, instead of relying solely on its internal training data, it retrieves relevant information (from databases, vector stores, or files) and integrates it into the generation process. This allows the model to provide precise answers based on real data, not just guesses.

Here’s how RAG works: when a user sends a query, the system first converts the query into an embedding vector and searches a knowledge base (like PGVector or a document store) for similar or relevant pieces of information. These retrieved snippets are then combined with the original query and passed into the model for final response generation. This two-step process ensures that the output is both contextually accurate and grounded in real data.

Disclaimer: This post is part of our comprehensive guide “Building an AI Assistant: Essential Tools and Concepts”. Each topic, including this one, is…