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 ...

We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, ...

In the world of artificial intelligence, the ability to build Large Language Model (LLM) and Retrieval Augmented Generation (RAG) pipelines using open-source models is a skill that is increasingly in ...

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 ...

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 ...

Retrieval augmented generation, or 'RAG' for short, creates a more customized and accurate generative AI model that can greatly reduce anomalies such as hallucinations. As more organizations turn to ...

First-party data has long been in a marketer’s toolkit as a critical instrument to personalize the customer experience across media touch points. But it has yet to influence how most companies use ...

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 ...

Companies investing in generative AI find that testing and quality assurance are two of the most critical areas for improvement. Here are four strategies for testing LLMs embedded in generative AI ...