Everyone loves retrieval-augmented generation (RAG). It has revolutionised how AI systems process and respond to user queries by leveraging external knowledge sources. At the same time, everyone wants ...

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

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“Agentic architectures are critical for the next wave of AI applications that can “do” tasks rather than just retrieve information. As teams push the first wave of RAG applications into ...

Bonaci said that, to date, RAG has been the method widely agreed upon in the industry as the most effective way to make generative AI relevant for an enterprise. “We do see people fine-tuning ...

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Retrieval-Augmented Generation (RAG) combines large language models (LLMs) with information retrieval techniques. The key objective is to connect a model’s built-in knowledge with the vast and ...