How can I learn prompt engineering?
Treat prompt engineering like a craft: learn core patterns, then practice deliberately on real tasks. Start with fundamentals (role, context, constraints, output format), then add techniques like few-shot, chain-of-thought, skeleton prompts, and self-critique loops. Rotate through varied tasks—summarization, data extraction, ideation, QA—to see how small changes shift outputs. Keep a lab notebook: record prompts, results, and tweaks so you can reuse what works. Finally, test across models (GPT-style, Claude-style, small LLMs) because behavior differs. Progress is less about memorizing magic words and more about learning to diagnose what went wrong and iterating quickly.
In practice
Hold quality bars constant (e.g., accuracy rubric, tone checklist) and iterate prompts against the same test set. This isolates what truly improves results.
Where to start
- [01]Work through a structured fundamentals guide and build 10 reusable templates.
- [02]Create a small benchmark dataset (10–20 examples) for your domain.
- [03]Run A/B prompt tests weekly; log wins/losses and why.
- [04]Try the same task on at least two model families to learn differences.