Retrieval Augmented Generation (RAG) is a groundbreaking development in the field of artificial intelligence that is transforming the way AI systems operate. By seamlessly integrating large language ...

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 ...

Large language models (LLMs) like OpenAI’s GPT-4 and Google’s PaLM have captured the imagination of industries ranging from healthcare to law. Their ability to generate human-like text has opened the ...

Every few months, the enterprise AI conversation resets around the same flawed premise that better models solve the problem. When large language models hallucinate, the instinct is to reach for a ...

If you’ve ever used a generative artificial intelligence tool, it has lied to you. Probably multiple times. These recurring fabrications are often called AI hallucinations, and developers are ...

Confused by AI jargon? We break down hallucinations, tokens, fine-tuning, and RAG in plain English for Ghanaian ChatGPT users ...

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.

How to implement a local RAG system using LangChain, SQLite-vss, Ollama, and Meta’s Llama 2 large language model. In “Retrieval-augmented generation, step by step,” we walked through a very simple RAG ...