Fine-tuning feels like the natural next step when your model isn’t performing — but it’s often the wrong one. Before committing to a training run, it’s worth asking: have you fully exhausted what you can achieve without touching the weights?

In this talk, we’ll break down the tradeoffs between prompt optimization and fine-tuning — when each approach earns its cost, and what the signals look like in practice. We’ll make it concrete using Weights & Biases Models and Weave, walking through a real evaluation workflow that tracks experiments, surfaces behavioral differences, and helps you measure whether a change actually moved the needle.
There’s no universal answer to which approach wins. But there is a better way to find out — and it starts with having the right evals in place before you make the call.