A local AI for your own documents can be really useful: Your own chatbot reads all important documents once and then provides the right answers to questions such as: or If you are a fan of board games ...

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

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document ...

However, when it comes to adding generative AI capabilities to enterprise applications, we usually find that something is missing—the generative AI programs simply don't have the context to interact ...

AI tends to make things up. That’s unappealing to just about anyone who uses it on a regular basis, but especially to businesses, for which fallacious results could hurt the bottom line. Half of ...

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 ...

What if the way we retrieve information from massive datasets could mirror the precision and adaptability of human reading—without relying on pre-built indexes or embeddings? OpenAI’s latest ...

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 ...

Some APIs rely instead on rules to block harmful content or requests for access to confidential documents for certain users. Opinion-gathering frameworks can also be used to refine the RAG ...