What if the way we retrieve information from massive datasets could mirror the precision and adaptability of human reading—without relying on pre-built indexes or embeddings? OpenAI’s latest ...

The OpenAI Responses API is a robust and versatile tool designed to streamline the development of Retrieval-Augmented Generation (RAG) systems. By automating intricate processes such as document ...

Retrieval Augmented Generation (RAG) is supposed to help improve the accuracy of enterprise AI by providing grounded content. While that is often the case, there is also an unintended side effect.

Retrieval-augmented generation is a standard way to ground large language models in enterprise information, but new research ...

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).

You know the ritual. The pipeline hallucinated in front of a customer, so you swapped the embedding model. Then you upgraded ...

Discover how to architect scalable RAG pipelines for enterprise Chat GPT deployments, focusing on best practices and key considerations.

Opinion-gathering frameworks can also be used to refine the RAG architecture. In this case, users are invited to rate the results in order to identify the positive and negative points of the RAG ...

Traditional RAG typically retrieves relevant text from a vector database and supplies it to an LLM as context. Automation ...

To operate, organisations in the financial services sector require hundreds of thousands of documents of rich, contextualised data. And to organise, analyse and then use that data, they are ...