RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...
Retrieval-Augmented Generation (RAG) connects large language models to external knowledge sources so they can deliver up-to-date, source-backed answers. By retrieving relevant documents at query time, ...
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 ...
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 ...
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 ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...
If you are interested in learning more about how to use Llama 2, a large language model (LLM), for a simplified version of retrieval augmented generation (RAG). This guide will help you utilize the ...
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 ...
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
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 ...
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