System
Generate Code From a Spec Without the Doom-Prompting Loop
Stops the AI from confidently guessing your tech stack, skipping edge cases buried in the spec, and dumping code that…
AI Prompts for Spec-Driven Development
Handing an AI a spec and asking it to "build this" is how you end up with Express and JWT-in-localStorage against a Fastify and httpOnly-cookie project: confident code that ignores your actual stack and quietly skips the edge cases buried in paragraph three. These AI prompts for spec-driven development force the model to restate the spec back to you, surface every assumption and ambiguity, and confirm your real codebase conventions before it writes a single line.
Instead of one giant, unreviewable code dump, the output comes in reviewable steps you can check against the spec as you go. That is the difference between an AI that "implemented the feature" and one that implemented what you actually asked for. Works with ChatGPT, Claude, and Gemini.
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Frequently asked questions
Why does AI guess the wrong tech stack when implementing a spec?
Because if the spec does not spell out every technical detail, the model fills the gap with whatever is statistically most common in its training data, which is often not your stack. Forcing it to explicitly list its assumptions about your stack, and confirm them against your actual codebase, catches this before it writes code against the wrong framework.
How do I stop AI from skipping edge cases in a spec?
Ask it to enumerate edge cases and acceptance criteria as a separate step before writing any code, and to flag anything the spec leaves ambiguous rather than silently picking an interpretation. Models tend to implement the happy path unless you explicitly require the edge-case list first.
Can AI review its own code against the original spec?
Yes, and it is worth doing as a final step: paste the spec and the generated code back in and ask it to check each requirement and acceptance criterion off individually. It catches gaps reliably when comparing two documents; it is far less reliable when generating and validating in the same breath.
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