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 a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...

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, ...

Local LLMs are fantastic, and they keep getting better at a staggering pace. I have non-negotiable reasons for preferring a local setup over relying on cloud giants like Claude or ChatGPT. Because of ...

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 ...

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 ...

Large language models (LLMs) demonstrate significant potential in healthcare applications, but clinical deployment is limited by privacy concerns and insufficient medical domain training. This study ...

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 ...

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 ...

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 ...