Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
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We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.
In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.
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⏯ Human Centered AI: Shaping a Just and Ethical Future in Health Technology @ Global Summit 2024
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⏯ Prompt before the prompt - third step of GenAI model training
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Generative artificial intelligence is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.
Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language.
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We continue with this series of articles on LLM and RAG applications and in this article we will discuss the red boxed part of the following diagram:
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In the process of creating a RAG application, choosing an LLM model that is appropriate to your needs (trained in the corresponding subject, costs, speed, etc.) is as important as having a clear understanding of the context you want to provide. Let's start by defining the term to be clear about what we mean by context.
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Watch this video to learn how InterSystems partner Mirus uses InterSystems IRIS to deliver clean data from multiple systems and feed a robust AI model that ensures that providers receive full funding for each resident while reducing administrative burden:
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hi everyone,
I'm trying to make some use of AI to help with coding. To achieve this I've moved to VS Code linking to a cache instance, and installed the "Cody" extension from Sourcegraph that talks to Claude Sonnet.
In theory Cody can view, and send to the AI, the file I'm currently editing. However, it does not seem to "see" Cache server items, and only works for stuff in ordinary files that I have open. Is this an issue with "isfs" ("InterSystems File Service" that implements the "filesystem provider API" )? Is anyone else using Cody or any other AI extension successfully?
Thanks,
Mike
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What components and libraries can you add to your retrieval-augmented generation (RAG) applications? Find out in this video:
Identifying Useful Components for Your Generative AI Application
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⏯ InterSystems IRIS Vector Search and the Python Ecosystem @ Global Summit 2024
In today's data landscape, businesses encounter a number of different challenges. One of them is to do analytics on top of unified and harmonized data layer available to all the consumers. A layer that can deliver the same answers to the same questions irrelative to the dialect or tool being used. InterSystems IRIS Data Platform answers that with and add-on of Adaptive Analytics that can deliver this unified semantic layer. There are a lot of articles in DevCommunity about using it via BI tools.
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Curious how a retrieval-augmented generation (RAG) application works? Take a look at this demonstration:
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Ready to create a retrieval-augmented generation (RAG) architecture for your next GenAI application? See how to get started:
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Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:
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How can you get your data ready for generative AI applications? Get some key tips from an InterSystems expert:
