One of the main problems with language models is that, although they "know a lot," their knowledge is frozen at the moment they were trained. This is where RAG (Retrieval-Augmented Generation) comes in.
How does RAG work?
RAG is a technique that combines the power of LLM with external databases. Before generating a response, the model searches an up-to-date data source (such as documents, websites, vector databases, etc.) and then generates the text using that information as context.
Imagine a ChatGPT that, before answering you, consults your internal documents or your company's technical documentation.
Advantages?
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Accuracy and timeliness : the answers are based on fresh data, not pre-trained knowledge.
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Customization : You can integrate your own documents.
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Reduction of hallucinations : if the model "sees" the information directly, the risk of making it up is reduced.
This technology is the basis of tools such as Perplexity AI, ChatGPT with attachments, and generative search engines like the new Bing or ChatGPT Browse.
👉 If you want to learn how to implement it, check out our article on how to create a custom search bar in ChatGPT .