Over the past few years, large language models (LLMs) have been everywhere. From chatbots that help you book flights to tools that summarize long documents, companies are finding ways to use LLMs in real products. But success is not guaranteed. In fact, sometimes it goes very wrong. A famous example was when Expedia’s chatbot once gave instructions on how to make a Molotov cocktail (Cybernews Report; see the chatbot screenshot below). Another example was Air Canada’s AI-powered chatbot making a significant error by providing incorrect information regarding bereavement fares (BBC Report). Mistakes like these show how important it is for industry practitioners to build strong evaluation systems for LLMs.

Recently, I read a blog post from GoDaddy’s engineering team about how they evaluate LLMs before putting them into real-world use (GoDaddy Engineering Blog). Their approach stood out to me because it was more structured than just running a few test questions. Here are the main lessons I took away:

1. Tie evaluations to business outcomes
   Instead of treating testing as an afterthought, GoDaddy connects test data directly to golden datasets. These datasets are carefully chosen examples that represent what the business actually cares about.
2. Use both classic and new evaluation methods
   Traditional machine learning metrics like precision and recall still matter. But GoDaddy also uses newer approaches like “LLM-as-a-judge,” where another model helps categorize specific errors.
3. Automate and integrate evaluation into development
   Evaluation isn’t just something you do once. GoDaddy treats it as part of a continuous integration pipeline. They expand their golden datasets, add new feedback loops, and refine their systems over time.

As a high school student, I’m not joining the tech industry tomorrow. Still, I think it’s important for me to pay attention to best practices like these. They show me how professionals handle problems that I might face later in my own projects. Even though my experiments with neural networks or survey sentiment analysis aren’t at the scale of Expedia, Air Canada, or GoDaddy, I can still practice connecting my evaluations to real outcomes, thinking about error types, and making testing part of my workflow.

The way I see it, learning industry standards now gives me a head start for the future. And maybe when I get to do college research or internships, I’ll already be used to thinking about evaluation in a systematic way rather than as an afterthought.

— Andrew