{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/94f5345736d34af3b8b6b41e1be4c2a3\" frameborder=\"0\" width=\"1114\" height=\"835\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":835,"width":1114,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":835,"thumbnail_width":1114,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/94f5345736d34af3b8b6b41e1be4c2a3-00001.gif","duration":282.333,"title":"Quick Guide for Prompt Learner","description":"https://github.com/attuna-xyz/prompt-learner Hey guys, welcome to the quick guide for Prompt Learner! In this video, I'll show you how to modularize and optimize your work with prompts in large language models. With Prompt Learner, you can break down complex prompts into manageable parts and experiment programmatically and efficiently. I'll walk you through the different modules, such as the task module and the prompt examples module, and show you how to use them. By the end of this video, you'll be able to classify customer text as urgent or not urgent using Prompt Learner. So let's get started!"}