S3 decouples RAG search from generation, boosting efficiency and generalization for enterprise LLM applications with minimal data.

“RAG allows you to do things like role-based access control,” explains Saunders. “When you’re connecting to a data source, ...

Getting retrieval-augmented generation right requires a deep understanding of embedding models, similarity metrics, chunking, ...

Google's "sufficient context" helps refine RAG systems, reduce LLM hallucinations, and boost AI reliability for business ...

Learn how OpenAI’s index-free RAG system uses dynamic retrieval and long-context models to revolutionize AI-driven ...

This guide will walk you through the process of building a RAG system from the ground up, covering key concepts, implementation steps using Python and TypeScript, and introducing pre-built tools ...

When a user submits a query, a RAG system first retrieves the most relevant information from a curated knowledge base. It then feeds this information, along with the original query, into the LLM.

It's important to note that a RAG system isn't 'fact-checking,' or doing any additional corroboration, but rather basing the answer to a prompt on additional context. That's why Jeremy Pickens ...

"The RAG system may still struggle to provide accurate information to users in cases where the context provided falls beyond the scope of the model's training data," they write. The LLM would at ...

While RAG is a powerful approach, it’s not a silver bullet. Its effectiveness depends on the quality of the retrieval system and the data being used. If the retrieval system fails to find ...