Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment

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AI Evaluation Plus Experimentation

Introduction

AI evaluation is the process of systematically assessing a model/system’s performance to ensure it meets specific task requirements using metrics such as accuracy, reliability, cost, and scalability. The goal is to measure what matters (easier said than done, I know). Of course, to know what matters, it matters to know how LLMs are contributing to the AI workloads.

At the end of the day, no matter how well we think our AI applications are working, nothing can compare to good old-fashioned testing. Evaluating LLMs and AI applications is, in general, a nebulous task that demands attention and proper context. There is no one best way to evaluate a model or a system. However, we can bucket the types ...