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 ...

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

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 ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

Ah, the intricate world of technology! Just when you thought you had a grasp on all the jargon and technicalities, a new term emerges. But you’ll be pleased to know that understanding what is ...

Retrieval Augmented Generation: What It Is and Why It Matters for Enterprise AI Your email has been sent DataStax's CTO discusses how Retrieval Augmented Generation (RAG) enhances AI reliability, ...

The last year has definitely been the year of the large language models (LLMs), with ChatGPT becoming a conversation piece even among the least technologically advanced. More important than talking ...

In the communications surrounding LLMs and popular interfaces like ChatGPT the term ‘hallucination’ is often used to reference false statements made in the output of these models. This infers that ...

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 ...