A large language model (LLM) is an artificial intelligence model designed to understand and generate human-like text based on vast amounts of training data.
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.
With rapid evolution of Generative AI, to embrace it and help us improve productivity is a must. Let's discuss and embrace the ideas of how we can leverage Generative AI to improve our routine work.
I implemented a Python Flask application for the 2024 Python Contest with a page that provides common form fields for an outgoing email such as the To and CC fields. And it lets you input a message as well as uploading text based attachments.
Then using LlamaIndex in Python, the app analyzes the content you put in and returns to you in a result box if there is anything that should stop you from sending that email.
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.
Watch this video to learn a new innovative way to use a large language model, such as ChatGPT, to automatically categorize Patient Portal messages to serve patients better:
https://www.youtube.com/embed/D0V09aGZK1E [This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]
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Open Exchange app
