Retrieval-Augmented Generation (Gemini)
RAG is an AI framework that enhances large language models (LLMs) by connecting them to external, up-to-date knowledge bases, rather than relying solely on their static training data.
This technique improves the accuracy, relevance, and contextuality of LLM outputs by allowing them to retrieve specific information from sources like internal databases, documents, or the internet before generating a response.
RAG increases trust by enabling the inclusion of citations and reduces the need for costly model retraining, making generative AI more adaptable and efficient for specific tasks.
How RAG Works
The process generally follows three main steps:
Retrieval: When you ask a question, the system searches a specific database (like your company’s private documents or a collection of recent news articles) for relevant snippets of information.
Augmentation: It takes those snippets and "staples" them to your original prompt, providing the model with extra context.
Generation: The LLM reads your question plus the retrieved facts to write a grounded, accurate response.
Why is it useful?
Current Information: You don't need to retrain a massive model to teach it new things; you just update the documents in the retrieval database.
Privacy and Security: Organizations can use RAG to let an AI interact with sensitive internal data without that data ever being used to train the public model.
Reduces Hallucinations: Because the model has facts right in front of it, it’s less likely to "make things up."
Links: Vectors