The Problem With Pure Generation

Why LLMs Sometimes Hallucinate

The Idea Behind Retrieval-Augmented Generation

How the RAG Workflow Works

1. User Question

2. Retrieval Step

3. Augmented Prompt

4. Response Generation

Benefits of RAG

Up-to-Date Information

Source-Based Answers

Reduced Hallucinations

Enterprise Applications

The New Challenge: Retrieval Quality

HexmosTech / git-lrc

Free, Unlimited AI Code Reviews That Run on Commit

git-lrc

Free, Unlimited AI Code Reviews That Run on Commit

See It In Action 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. 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. This works surprisingly well in many cases. However, it introduces two major problems: Consider a simple example. Imagine asking an AI assistant: “What is the pricing of the OpenAI API?” The model might confidently respond with pricing that was correct months ago. But APIs, products, and services change frequently. If the model was trained before a recent pricing update, the answer will be outdated. Even worse, the model might present the answer confidently without referencing any official documentation. This creates a serious issue: users cannot easily verify whether the information is correct. Traditional LLMs rely entirely on the knowledge stored in their parameters. When they generate an answer, they are not actually checking external sources. The process typically looks like this: If the model’s training data contained outdated or incomplete information, the answer may be incorrect. This behavior is often referred to as hallucination. The model is not intentionally lying, it is simply generating the most statistically likely response. Retrieval-Augmented Generation (RAG) improves this process by allowing the model to retrieve information before generating an answer. Instead of relying only on training data, the system connects the language model to a content store. This store could contain: When a user asks a question, the system first searches this content store to find relevant information. Only after retrieving relevant documents does the model generate the final response. The RAG process usually follows these steps. A user asks a question. “What is the pricing for the OpenAI API?” The system searches a database or document collection to find relevant information. It might retrieve the latest pricing details from official documentation. The retrieved documents are combined with the user’s question and passed to the language model. The prompt now contains three components: Using the retrieved information as context, the model generates a response grounded in real data. Instead of guessing the answer, it now references the retrieved information to provide a more accurate response. One of the biggest advantages of RAG is that it allows AI systems to stay current without retraining the entire model. If new information appears, such as updated pricing or new documentation — you simply update the data store. The model will retrieve the latest information when answering future queries. RAG allows systems to provide answers backed by actual documents. Instead of just stating an answer, the system can reference the source it used. This makes responses more trustworthy and easier to verify. Because the model relies on retrieved documents, it is less likely to invent information. If the system cannot find relevant data, it can respond with: “I don’t have enough information to answer that.” This behavior is far safer than generating a misleading answer. RAG is particularly useful in organizations where AI systems must answer questions using internal knowledge. For example, companies use RAG to build assistants that answer questions about: The model retrieves information from the company’s private documents before generating responses. While RAG improves accuracy, it introduces a new dependency: the quality of the retrieval system. If the system retrieves poor or irrelevant documents, the model may still generate weak responses. This is why researchers focus on improving both sides of the system: *AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.* Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use. | 🇩🇰 Dansk | 🇪🇸 Español | 🇮🇷 Farsi | 🇫🇮 Suomi | 🇯🇵 日本語 | 🇳🇴 Norsk | 🇵🇹 Português | 🇷🇺 Русский | 🇦🇱 Shqip | 🇨🇳 中文 | AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free. See git-lrc catch serious security issues such as leaked credentials, expensive cloud operations, and sensitive material in log statements Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to ? It will become hidden in your post, but will still be visible via the comment's permalink. as well , this person and/or - The model may rely on outdated knowledge - The model may provide answers without reliable sources - A user asks a question. - The model analyzes the prompt. - It generates a response based on patterns learned during training. - Documentation - Knowledge bases - Company policies - Product manuals - Instructions for the model - Retrieved content - The user’s question - Internal documentation - HR policies - Product manuals - Engineering knowledge bases - Better retrievers to find the most relevant information - Better generators to produce clear and accurate responses - 🤖 AI agents silently break things. Code removed. Logic changed. Edge cases gone. You won't notice until production. - 🔍 Catch it before it ships. AI-powered inline comments show you exactly what changed and what looks wrong. - 🔁 Build a…