Learning path · Foundations · 04
Prompting
Structuring instructions, context, and examples so the model produces useful, controllable outputs for a specific task.
Why it matters
- Prompt quality often moves outcomes more than swapping model families.
- Clear roles, constraints, and formats reduce ambiguity and rework.
- Prompting is the first control surface before orchestration gets complex.
Key ideas
- Task specification
- Constraints
- Format control
- Iteration
A prompt is a specification, not incantation. State the role, audience, task, constraints, and desired output shape. When results fail, change one variable at a time—examples, tone, delimiters, or evidence ordering. Strong prompting is editorial judgment applied to model inputs. In production, prompts belong in version control with evaluation sets so changes are measurable, not tribal knowledge buried in a dashboard text box. Pair prompt changes with regression evals on representative failures from support logs, not only on cherry-picked success stories from the lab.
Updated 2026-08-09 · Full learning path