This course is aimed at developers familiar with Node.js and APIs who have built an LLM-based app and are ready to take it to the next level by improving its accuracy, performance, and scalability for production use.

This course teaches you how to take a prototype AI application and optimize it for production. You’ll learn how to set up evaluation processes, including metrics, scores, and datasets. You’ll explore techniques like prompt engineering, Retrieval-Augmented Generation (RAG), and hyperparameter adjustments to improve the performance of large language models. Additionally, we’ll cover practical strategies for handling token limits, reducing latency, improving speed, and keeping costs under control. While fine-tuning will be discussed, the focus will be on optimization techniques that don’t require it. By the end of this course, you’ll be equipped to take your AI app from prototype to production.