Pick a depth. Each prompt opens in your AI pre-loaded with the lesson. Click a row to preview the prompt.
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.
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.
def writeup(project, decision, metric, baseline, threshold, data_plan):
return f'''FINE-TUNE GO/NO-GO -- {project}
Decision: {decision}
Target metric: {metric}
Baseline first: measure best-prompt = {baseline} BEFORE training
Success bar: {threshold}
Data plan: {data_plan}
No-regression: define one capability that must not drop'''
print(writeup(
project="Support-ticket summarizer",
decision="Fine-tune small model (LoRA) + RAG for current policy",
metric="human-rated summary usefulness (1-5) on 100 held-out tickets",
baseline=3.4, threshold=4.2,
data_plan="Curate 600 clean (ticket -> summary) pairs from resolved tickets"))python3 main.py