Prototype to Production: Advanced AI Apps

This course will teach you how to transition an AI application prototype into a fully-fledged product. You will learn how to set up evaluation processes, including metrics, indicators, and datasets. You will explore techniques such as prompt tuning, Retrieval-Augmented Generation (RAG), and hyperparameter tuning to enhance the performance of large language models. Additionally, we will cover practical strategies for managing token limits, reducing latency, increasing speed, and controlling costs. While we will discuss fine-tuning, the focus will be on optimization techniques that do not require it. By the end of the course, you will be equipped to take your AI application from prototype to full-scale production launch.

During the participation in the master class, you will learn to:

• Understand how to launch and evaluate AI applications for use in production.
• Set up evaluation metrics, scoring systems, and datasets (both synthetic and real) for accurate assessment.
• Explore methods such as prompt tuning, RAG, and hyperparameter tuning to improve LLM performance accuracy.
• Master strategies for dealing with token limitations, optimizing speed, reducing latency, and cutting costs.
• Use structured outputs to obtain deterministic values from LLM.
• Classify user input before responding to enhance accuracy and efficiency.