#Generative AI (GenAI)

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.

Hi Community!

Ceck out the new video dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Keeping your data private and protected with RAG

https://www.youtube.com/embed/ucpPgc5kNKc
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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.

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:

Hi Community!

We're happy to share the next video in the series dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Pre-training - first step of GenAI model training

https://www.youtube.com/embed/cZw3LNfPB0M
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Hey Developers,

Play the new video on InterSystems Developers YouTube:

⏯ Chatbots are all the RAGe: Generative AI with InterSystems IRIS @ Global Summit 2024

https://www.youtube.com/embed/mj6NshZ4T0U
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Hey Community,

We're excited to introduce a new series of videos dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ 3 GenAI Model Training Opportunities

https://www.youtube.com/embed/L24FZfvryd8
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Hi, Community!

Ready to create a retrieval-augmented generation (RAG) architecture for your next GenAI application? See how to get started:

Setting Up Your Generative AI Development Environment

https://www.youtube.com/embed/CzyQVtgdmiA?utm_source=youtube&utm_medium=social&utm_campaign=CzyQVtgdmiA
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Hi, Community!

Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:

Exploring the Benefits of RAG

https://www.youtube.com/embed/NSYVHiJPRas?utm_source=youtube&utm_medium=social&utm_campaign=NSYVHiJPRas
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Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

• Ingest Documents (PDF or TXT) into IRIS
• Chat with the selected Ingested document
• Delete Ingested Documents
• OpenAI ChatGPT

I received some really excellent feedback from a community member on my submission to the Python 2024 contest. I hope its okay if I repost it here:

Hi, Community!

Do you think Generative AI can make your life easier? See some potential use cases for GenAI in the latest video from Learning Services:

▶ Generative AI in Use

https://www.youtube.com/embed/5D8A0PDsV2A?utm_source=youtube&utm_medium=social&utm_campaign=5D8A0PDsV2A
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