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’s power is premised on cortical building blocks. Retrieval-Augmented Generation (RAG) is one of such building blocks enabling AI to produce trustworthy intelligence under a given condition.

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 ...

COMMISSIONED: Retrieval-augmented generation (RAG) has become the gold standard for helping businesses refine their large language model (LLM) results with corporate data. Whereas LLMs are typically ...

Many medium-sized business leaders are constantly on the lookout for technologies that can catapult them into the future, ensuring they remain competitive, innovative and efficient. One such ...

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 ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...

The rapid growth of data has intensified security risks for Large Language Model (LLM) use cases handling sensitive information. Thales addresses these challenges with the CipherTrust Data Security ...

AI teams are discovering that the most expensive part of an agent is not the model, it is the memory strategy wrapped around it. Retrieval-Augmented Generation has become the default pattern for ...

Tests on GPT and Claude found they ignored invented spells Fumbus and Driplo; training data can override new input, trust ...