Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform ...

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 ...

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 ...

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 ...

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...

Performance. Top-level APIs allow LLMs to achieve higher response speed and accuracy. They can be used for training purposes, as they empower LLMs to provide better replies in real-world situations.

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 ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More To scale up large language models (LLMs) in support of long-term AI ...