Choosing and Evaluating LLM Models

What you'll build

• A reusable evaluation harness that scores several LLMs on the same task, side by side
• A benchmark you can re-run as new models ship, so your model choice stays current
• A model-selection scorecard that weighs accuracy, cost, and latency for your own use case

What you'll learn

• How to design an evaluation that reflects your real task, not a public leaderboard
• Practical metrics for quality, cost, and speed, and how to trade them off honestly
• When a smaller or open model beats a frontier model for the job in front of you
• How to avoid the common traps: data contamination and overfitting to your own examples

About Nic

Open source developer, R educator, and consultant who maintains Apache Arrow and teaches data scientists how to work with LLMs in R.