Prompt Engineering & Structured Output
Constrain, don’t coax — the right answer is almost always the more specific one.
How you talk to the model and make its answers usable by a program. Its signature trap is that the wrong answers sound like good engineering — “raise the temperature,” “use the biggest model,” “fine-tune it.” The right instinct is almost always the more specific, constrained one: cast the model in a role, spell out measurable criteria, force the output shape with a tool schema, ground it in the source text, and verify the result in code.
6 task statements on the blueprint
- 4.1Design prompts with explicit criteria to improve precision and reduce false positives4 objectives · 4 exam traps · 2 primary sources · 16 practice questions
- 4.2Apply few-shot prompting to improve output consistency and quality4 objectives · 5 exam traps · 2 primary sources · 5 practice questions
- 4.3Enforce structured output using tool use and JSON schemas4 objectives · 5 exam traps · 2 primary sources · 21 practice questions
- 4.4Implement validation, retry, and feedback loops for extraction quality4 objectives · 4 exam traps · 2 primary sources · 6 practice questions
- 4.5Design efficient batch processing strategies5 objectives · 5 exam traps · 2 primary sources · 9 practice questions
- 4.6Design multi-instance and multi-pass review architectures3 objectives · 4 exam traps · 2 primary sources · 4 practice questions
The other domains
Working the whole blueprint? The interactive study guide carries the concept cards, the mock quiz, the audio lessons and the practice simulation.