Bridging Static Knowledge and Dynamic Context in AI
Explore how retrieval-augmented generation (RAG) integrates static knowledge from large language models with dynamic external information. Understand the process of combining retrieval and generation to produce relevant, up-to-date, and coherent AI responses. Learn about RAG's design, benefits, limitations, and future advancements enabling intelligent and reliable interaction with AI systems.
We'll cover the following...
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 ...