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The Prompt Is Now Part of the Spec

Practice turning a real, framed problem into a build prompt that specifies the states and constraints a Product Engineer already knows matter, then reviewing the AI's first result against that spec instead of accepting it at face value.

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Prompt as Spec

Ambiguity you skip becomes a guess the model makes.

Quick Quiz //

Why should a build prompt for an AI coding tool specify the empty and error states explicitly?


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AI coding tools didn't remove the need to specify what you're building precisely — they just moved that specification into the prompt itself.

1A Vague Prompt Produces a Vague Result

Everything you'd need to specify in a mini-PRD — the exact data, the empty state, the error state — still needs to be specified when prompting an AI coding tool. Skipping it doesn't remove the ambiguity, it just defers it to whatever the model happens to guess.

2Treat the First Result as a Draft, Not a Delivery

AI-generated code that looks plausible at a glance can still silently skip a state you asked for. Reviewing the result against your original spec — did it handle empty, error, and loading — is a required step, not an optional extra.

3Step-by-Step Breakdown

From a One-Sentence Problem to a Real Prototype. This module is hands-on. You'll take a real, small problem — a settings page needs a way to let users export their data as CSV — and drive an AI coding tool from a plain-language prompt to a working prototype, then judge the result like a Product Engineer, not just accept it.

Turn a Problem Statement Into a Build Prompt. A vague prompt gets a vague result. Ask the model to build a small React component for CSV export, but give it the real constraints a Product Engineer would already know: what data, what format, what happens on failure.

Why does specifying the empty-list and failure cases in the prompt matter, instead of just asking for 'CSV export'?

  • Those are exactly the empty/error states from an earlier lesson — leaving them unspecified means the model has to guess, and guesses are where bugs and bad UX creep in.
  • It just makes the prompt longer, which AI models generally prefer regardless of content.

Push Back on a Plausible-Looking First Result. AI-generated code often looks done at a glance but skips something you actually specified. Ask the model to also add a loading state while the CSV is being generated, and to confirm explicitly whether its first version already handled the empty-list case correctly.

Speed Didn't Remove Your Job — It Moved It. Notice what just happened: the AI wrote the code in seconds, but you still did real Product Engineer work — specifying the real constraints, and reviewing the result against them. That review step is exactly what the next lesson digs into: reviewing AI-generated code like it's your own.

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Advanced cheat sheets, SEO tricks, and interview prep for this topic.

Browser Support

ChromeSupported

Fully supported.

FirefoxSupported

Fully supported.

SafariSupported

Fully supported.

EdgeSupported

Fully supported.

Accessibility (A11y)

1Specify Accessibility Requirements in the Prompt Explicitly

AI coding tools won't reliably add correct focus management or ARIA labeling unless asked — include it as an explicit requirement in the prompt, the same way you'd include the empty-list or error-handling requirement.

"...and make sure the export button has an accessible loading announcement via aria-live."

SEO Implications

  • 1

    Target 'how to prompt AI coding tools for real features' rather than generic 'AI coding tips'

    Readers want the specific discipline of translating a spec into a prompt, not a general list of prompt engineering tricks.

Best Practices

Reuse Your Mini-PRD Fields as Prompt Ingredients

The problem, audience, and success metric from your mini-PRD translate almost directly into prompt context — reusing them keeps the AI-generated build aligned with the actual decision you already made, instead of starting from scratch.

Frequent Bugs

THE BUG

Accepting an AI-generated component's first version without checking it against the specific states (empty, error, loading) that were actually requested.

THE FIX

After generating code, explicitly re-read the original prompt's requirements one by one against the result — treat any unaddressed requirement as an unfinished task, not an acceptable omission.

Real-World Examples

The Missing Empty State

A first AI-generated version of the CSV export button worked perfectly with data present, but crashed with a confusing error when the list was empty — because the prompt hadn't specified that case. A follow-up prompt naming the empty-list requirement fixed it in one more round.

// First prompt: "add a CSV export button" -> crashes on empty list
// Better prompt: explicitly names the empty-list case -> handles it correctly

Interview Prep

?Frequently Asked Questions

Pascual Vila

Pascual Vila

Full-Stack Software and AI Engineer

Full-Stack Software and AI Engineer with 6 years of experience building enterprise-grade web applications across React, Angular, Node.js, and Python. Recently completed a Master's in AI Development specializing in LLMs, RAG, and AI agent architectures, and currently builds enterprise systems that integrate AI and Digital Twins to optimize industrial and logistics processes.

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Common Pitfalls & Errors

The Error //

Writing a vague, one-line prompt for a non-trivial feature and accepting whatever comes back

// Vague: "add CSV export" // Specified: "export list as CSV; disable button when empty; show error message on failure"

The Solution //

Include the real constraints — exact data, empty state, error state, success metric if relevant — directly in the prompt, and explicitly check the result against each one afterward instead of accepting a plausible-looking first draft.

Lesson Glossary

[01]Build Prompt

A prompt to an AI coding tool that specifies not just the desired feature, but its real constraints — exact data, empty state, error state — the same detail a spec would require.

Code Preview
prompt = feature + constraints (empty, error, loading)

[02]Spec Review

Explicitly checking AI-generated code against each requirement that was originally specified, rather than accepting a plausible-looking result at face value.

Code Preview
// Spec Review context

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