Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.

Vectara, an early pioneer in Retrieval Augmented Generation (RAG) technology, is raising a $25 million Series A funding round today as demand for its technologies continues to grow among enterprise ...

For generative AI to live up to its promise of transforming the enterprise, it first needs to meet the needs of the enterprise. Large language models need business-specific context to minimize ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...

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).

Much of the interest surrounding artificial intelligence (AI) is caught up with the battle of competing AI models on benchmark tests or new so-called multi-modal capabilities. But users of Gen AI's ...

A consistent media flood of sensational hallucinations from the big AI chatbots. Widespread fear of job loss, especially due to lack of proper communication from leadership - and relentless overhyping ...

General purpose AI tools like ChatGPT often require extensive training and fine-tuning to create reliably high-quality output for specialist and domain-specific tasks. And public models’ scopes are ...

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 ...