{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/173c11356f0342359b92975b0e3ede1a\" frameborder=\"0\" width=\"1664\" height=\"1248\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1248,"width":1664,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1248,"thumbnail_width":1664,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/173c11356f0342359b92975b0e3ede1a-1688736844966.gif","duration":602.868032241,"title":"Exploring Bench LLM: Evaluating and Comparing LLM-based Solutions 🧪","description":"Hey there, AI engineers! In this video, I'll be introducing you to Bench LLM, a Python-based open source library that is revolutionizing the testing of LLMs and AI-powered applications. Bench LLM is a powerful tool used for evaluating the accuracy of LLM-powered products. Today, we'll be using Bench LLM to evaluate and compare different LLM-based solutions, specifically GPT-3 and run chain agents. We'll be running a test suite designed to challenge their ability to compute 3-digit multiplications. Join me as we install Bench LLM, import the library, mark the functions we'd like to test, and prepare our test suite. I'll also show you how to set up your OpenAI API key for BenchLLM. By the end of this video, you'll have a clear understanding of how Bench LLM works and how it can enhance your AI testing process. Let's dive in and see how these LLM-based solutions perform! 🚀"}