A study released this month by researchers from Stanford University, UC Berkeley and Samaya AI has found that large language models (LLMs) often fail to access and use relevant information given to ...

Imagine trying to have a conversation with someone who insists on reciting an entire encyclopedia every time you ask a question. That’s how large language models (LLMs) can feel when they’re ...

Context window is like the LLM’s working memory. It is the amount of information an LLM (large language model) can use at one time while generating a response. A context window is measured in the ...

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

While the shortest distance between two points is a straight line, a straight-line attack on a large language model isn't always the most efficient — and least noisy — way to get the LLM to do bad ...

What if the solution to skyrocketing API costs and complex workflows with large language models (LLMs) was hiding in plain sight? For years, retrieval-augmented generation (RAG) has been the go-to ...

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 ...