Improving RAG: Strategies

RAG, which stands for "Retrieval Augmented Generation," is a strategy in artificial intelligence where a large language model (LLM) retrieves relevant information from an external knowledge base (like a database or document collection) before generating a response to a user query, ensuring the response is more accurate and contextually relevant by ... See more

a couple of the top of my head:

• LLM in the loop with preference optimization


• synthetic data generation


• cross modality "distillation" / dictionary remapping


• constrained decoding

This represents a fundamentally different way of thinking about IR systems. Within the index-retrieve-then-rank paradigm, modeling work (e.g., query understanding, document understanding, retrieval, ranking, etc.) is done on top of the index itself. This results in modern IR systems being comprised of a disparate mix of heterogeneous models (e.g., ... See more

You use state-of-the-art Retrieval Augmented Generation technology. How does that work?

Retrieval augmented generation (RAG) is an up and coming technique that addresses the pitfalls of large language models, particularly in the realm of AI hallucinations. It uses a mix of both good information retrieval and generative AI to generate answers which ... See more