Why AI Lies (And How RAG Fixes It)

Hello, I'm Maneshwar. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. Large language models (LLMs) are everywhere today. They power chatbots, coding assistants, and AI search tools. Sometimes they give incredibly accurate answers, and other times they produce responses that sound confident but are completely wrong. To address this problem, researchers developed a framework called Retrieval-Augmented Generation (RAG) . It helps AI systems produce answers that are more accurate, reliable, and up to date. Let’s understand why this approach is necessary. The Problem With Pure Generation Large language models generate responses based on the information they learned during training. When a user asks a question, the model analyzes the prompt and predicts the most likely sequence of words to form an answer. Th