RAG Explained: How to Give an LLM Your Own Information
A language model knows a lot about the world, but it knows nothing about your company: your manuals, your policies, your products. And if you ask it something it doesn't know, it can make up an answer that sounds convincing. RAG (Retrieval-Augmented Generation) solves both problems. The idea is that instead of expecting the model to "know" your information, you give it to it at the time of the question: You index your...
RAG (Retrieval-Augmented Generation) is a solution that enables language models to provide accurate answers based on a company's specific information, such as manuals, policies, and products. The approach involves indexing documents, converting them into numerical vectors, and then searching for relevant fragments when a question is asked.
These fragments are then used by the language model to generate a response, reducing the likelihood of fabricated answers. This method allows for the citation of sources, increasing trust and verifiability, and is considered a cost-effective way to apply AI in a business, as it doesn't require retraining the model.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
