Bridging Static Knowledge and Dynamic Context in AI
Learn how RAG seamlessly integrates retrieval and generation to augment LLMs with up-to-date, domain-specific information.
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Imagine a brilliant student who knows everything up to a certain year but can’t access any new books. That’s what happens with large language models (LLMs): their knowledge freezes after training. Retrieval-Augmented Generation (RAG) solves this by combining an LLM’s language skills with the ability to fetch up-to-date, external information in real time.
What is RAG?
Modern language models generate fluent, human-like text, but their knowledge is fixed at the time of training. Retrieval-Augmented Generation (RAG) solves this limitation by combining two strengths: retrieval and generation.
Instead of relying only on what’s stored in its parameters, RAG retrieves relevant information from an external knowledge base and then uses an LLM to generate an informed, coherent answer.
The idea, introduced ...