Amazon Web Services (AWS) has updated Amazon Bedrock with features designed to help enterprises streamline the testing of applications before deployment. Announced during the ongoing annual re:Invent ...

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

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 ...

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

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.

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 ...

All the large language model (LLM) publishers and suppliers are focusing on the advent of artificial intelligence (AI) agents and agentic AI. These terms are confusing. All the more so as the players ...

Prof. Aleks Farseev is an entrepreneur, keynote speaker and CEO of SOMIN, a communications and marketing strategy analysis AI platform. Large language models, widely known as LLMs, have transformed ...