Lesson 1684 of 2116
AI coding: spec-driven prompts that compile on the first pass
Hand the AI a tight spec — inputs, outputs, edge cases, error modes — and you get production-ready code instead of plausible mush.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2spec-driven prompting
- 3input/output contracts
- 4edge cases
Concept cluster
Terms to connect while reading
Section 1
The premise
AI coding tools generate stronger code when the prompt reads like a function spec rather than a wish. Listing inputs, outputs, edge cases, and error behavior up front cuts iteration loops dramatically.
What AI does well here
- Implement a function exactly to a typed signature you provide
- Enumerate happy-path and edge-case branches when you list them
- Generate matching tests when the spec includes expected behavior
What AI cannot do
- Guess unstated requirements correctly
- Know which edge cases your domain actually cares about
- Recover gracefully from a vague spec without re-prompting
Key terms in this lesson
End-of-lesson quiz
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