Auditing a real project

Why this matters

The skills in this module only stick when you apply them to something messy and real. Taking an actual project — yours or a public case study — and running it through the cost, data, anti-pattern, and decision-tree lenses turns abstract advice into a concrete go/no-go memo. This task is the module's capstone-in-miniature: produce the one-page recommendation you'd defend to a skeptical engineer, complete with the metric you'd target and the baseline you'd measure first. It's the difference between knowing the framework and being able to use it under pressure.

Demo

The demo is a template for the go/no-go memo: it pulls together the decision, the chosen metric, the baseline-first plan, and the data scope into a single artifact you can hand to a teammate.

Try it yourself

1. Write the go/no-go memo for one real project end to end.
2. Pick the single metric you'd defend to a skeptic and justify why it captures the goal.
3. Specify exactly how you'd measure the baseline before any training.
4. Scope the dataset to a number you could actually build this week.

Prompt your AI

Use these three in order. Each builds on the one before.

1. Basics & terminology

In one paragraph, what should a one-page 'should we fine-tune this?' memo contain?

2. Why it works (the mechanism)

Walk me through auditing a real project for fine-tuning readiness, from problem framing to baseline measurement.

3. Advanced — application & what's next

Here's my project: [describe it]. Produce a defensible go/no-go memo with the metric, baseline plan, data scope, and the strongest counterargument against fine-tuning it.

References

• Hugging Face — TRL documentation
• Hugging Face — PEFT documentation