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!

Need to learn how to write better prompts for GenAI? This video from Learning Services introduces six key strategies:

▶ Optimizing Your Prompts for Generative AI

https://www.youtube.com/embed/nBnm84cNMQo?utm_source=youtube&utm_medium=social&utm_campaign=nBnm84cNMQo
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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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Hi Community,

Watch this video to learn about the PainChek artificial intelligence technology, which assesses patient pain at the hospital bedside, leverages InterSystems IRIS interoperability to connect to third-party electronic medical record systems:

⏯ Using Artificial Intelligence / Machine Learning for Pain Assessment in a Clinical Environment @ Global Summit 2023

https://www.youtube.com/embed/-XNQUj7Sx_s
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Hey Community!

We're happy to share our newest learning path dedicated to the essentials of GenAI, accessible on the Learning Portal via your SSO:

🤖 Getting Started with Generative AI 🤖

Here is what you can expect:

Hi,

Is anyone using a 3rd party software for dictating notes, and has a way to integrate that with TrakCare?

We are exploring introducing an AI-scribe software for medical professionals note taking and wondering how to connect the two.

Thanks

We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.

Continuing with the series of articles on voice file management, we are going to see how we can convert text into audio and receive the file with the chosen voice.
We will also explore how a service from OpenAI can help us analyze a text and determine the mood expressed in it.
Let's analyze how you can create your own voice file and how it can “read” your feelings.

In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity.

TL;DR

This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.

Hi Community,

Here is a brief walkthrough on the capabilities of IRIS AI Studio platform. It covers one complete flow from loading data into IRIS DB as vector embeddings and retrieving information through 4 different channels (search, chat, recommender and similarity). In the latest release, added docker support for local installation and live version to explore.

https://www.youtube.com/embed/X1gzz3Qs2dw
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In the previous article, we saw different modules in IRIS AI Studio and how it could help explore GenAI capabilities out of IRIS DB seamlessly, even for a non-technical stakeholder. In this article, we will deep dive into "Connectors" module, the one that enables users to seamlessly load data from local or cloud sources (AWS S3, Airtable, Azure Blob) into IRIS DB as vector embeddings, by also configuring embedding settings like model and dimensions.

With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.

What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.