Beyond Benchmarks - Evaluating LLMs
Public benchmarks lie. Build your own evaluation pipeline that actually tests what matters for your application. Create golden datasets, define custom metrics, and use MLflow to systematically compare models and prompts.
When a new large language model is released, it's often accompanied by headlines celebrating its superior performance on public benchmarks. But those leaderboard scores can be deeply misleading. Relying on them is like hiring a candidate based solely on their GPA, without ever checking if they have the specific skills needed for the job. Models are often implicitly or explicitly optimized for these public tests, a phenomenon known as "benchmaxing", which doesn't guarantee their effectiveness for your unique tasks.
In this tutorial, we'll take the financial news dataset you built in the previous lesson and create a testing framework around it. You'll learn to build "golden set" evaluation data, define custom metrics that actually matter for your use case, and use MLflow to systematically compare models and prompts. No more guessing which LLM works best - you'll have the data to prove it.
By the end, you'll have a repeatable evaluation framework that turns model selection from guesswork into engineering.
Tutorial Goals
- A private evaluation pipeline that tests models on your actual use case
- Custom metrics that measure what actually matters for your application
- An automated testing framework using your financial news dataset
- MLflow experiment tracking to compare models and prompts systematically
- The ability to prove which model works best with data, not gut feelings