Introduction Retrieval-augmented generation (RAG) systems are transforming AI by enabling large language models (LLMs) to access and integrate information from external vector databases without needing fine-tuning. This approach allows LLMs…
Nanonets makes financial workflows more efficient with AI-driven automation, enabling businesses on Sage Intacct to automate manual finance tasks confidently and securely.San Francisco, 19 August 2024—Nanonets, a pioneer in AI-driven…
Tedious paperwork and long wait times are every person's worst insurance nightmare. But insurance claim automation is paving the way for faster, more accurate, and more customer-friendly experiences. The drive…
In this entry (Part 1) we’ll introduce the basic concepts for face recognition and search, and implement a basic working solution purely in Python. At the end of the article…
With consumer demand climbing, regulations tightening and the cost of doing business rising, facilities must optimize their packaging line efficiency or face losing to their competition. Could automation powered by…
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With the emergence of large language models, prompt engineering has become an essential skill. Put simply, prompting involves how humans interact with machines. Engineering…
Vision-language models (VLMs) have gained significant attention due to their ability to handle various multimodal tasks. However, the rapid proliferation of benchmarks for evaluating these models has created a complex…
Unlocking the power of large language models Photo by ZHENYU LUO on UnsplashIn this article, I will examine how large language models (LLMs) can convert natural language into SQL, making…
Introduction Retrieval Augmented Generation, or RAG, is a mechanism that helps large language models (LLMs) like GPT become more useful and knowledgeable by pulling in information from a store of…
In the AI space, where technological development is happening at a rapid pace, Retrieval Augmented Generation, or RAG, is a game-changer. But what is RAG, and why does it hold…