Applications requiring high-performance information retrieval span a wide range of domains, including search engines, knowledge management systems, AI agents, and AI assistants. These systems demand retrieval processes that are accurate and computationally efficient to deliver precise insights, enhance user experiences, and maintain scalability. Retrieval-augmented generation (RAG) is used to…
]]>Efficient text retrieval is critical for a broad range of information retrieval applications such as search, question answering, semantic textual similarity, summarization, and item recommendation. It also plays a pivotal role in retrieval-augmented generation (RAG), a technique that enables large language models (LLMs) to access external context without modifying underlying parameters.
]]>Enterprises are sitting on a goldmine of data waiting to be used to improve efficiency, save money, and ultimately enable higher productivity. With generative AI, developers can build and deploy an agentic flow or a retrieval-augmented generation (RAG) chatbot, while ensuring the insights provided are based on the most accurate and up-to-date information. Building these solutions requires not…
]]>The conversation about designing and evaluating Retrieval-Augmented Generation (RAG) systems is a long, multi-faceted discussion. Even when we look at retrieval on its own, developers selectively employ many techniques, such as query decomposition, re-writing, building soft filters, and more, to increase the accuracy of their RAG pipelines. While the techniques vary from system to system…
]]>Large language models (LLMs) are transforming the AI landscape with their profound grasp of human and programming languages. Essential for next-generation enterprise productivity applications, they enhance user efficiency across tasks like programming, copy editing, brainstorming, and answering questions on a wide range of topics. However, these models often struggle with real-time events and…
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