What Is Spec-Driven Development? 3 Mistakes That Stall AI Builds

An AI-generated feature can look finished on Friday and be difficult to extend on Monday. The original request is buried in chat, the expected behavior was never written down, and a small change starts breaking things you thought were settled. Spec-driven development addresses that gap by describing what the software should do before you ask a tool to build it, then updating that description as you learn. It works best when the spec is short enough to guide the next build session.

Why spec-driven development helps with AI

A spec is a written agreement with your future self about the feature you are building. For a small SaaS, it might name the user, the action they need to take, the data that must persist, and the conditions that show the feature works. A short product requirements document (PRD) follows the same pattern and can also state what happens when an action fails and what is out of scope. You can give that context to an AI coding tool, inspect its changes against the spec, and revise either the code or the spec when you discover a better approach.

The document does not have to predict every edge case. Its job is to stop a working prototype from becoming a collection of prompts with no shared target. Tools such as Kiro from AWS can draft requirements and tasks, but a generated spec is still a proposal to review. The mistakes below happen when the spec is treated as either a formality or a substitute for checking the software.

1: Writing a spec too vague to test

“Build a clean dashboard” gives an AI tool plenty of room to make design and product decisions for you. The result may look polished while leaving out the one action your user needs, such as finding an overdue invoice. Vague requests are tempting because they get code on the screen quickly, but they make it hard to tell whether the feature is done.

Write a few observable outcomes instead. For an invoice dashboard, you might specify that a signed-in user can see their invoices, distinguish paid from overdue items, and open one invoice to review its details. Ask the tool to build one outcome at a time. When you test it, you have a concrete behavior to check rather than a general impression of quality.

For help turning those outcomes into AI build requests, this step-by-step guide to prompt-driven development covers scoping prompts, providing context, and reviewing the result.

2: Treating the first spec as permanent

You may discover halfway through a build that users need to edit an invoice before sending it. If the spec still says invoices are created and sent in one action, the next prompt starts from an outdated assumption. The code and the written plan drift apart, and returning to the project becomes a guessing exercise.

Change the spec when the product decision changes. Record the new behavior, note why you chose it, and identify what existing behavior needs another test. This can take a few sentences. The useful part is that your next session starts with the decision you actually made, not the one you made before seeing the feature work.

3: Accepting code without checking it against the spec

A detailed request can still produce a broken implementation. A screen might show a success message even though the database write failed, or an edit action might update the wrong record. Reading the generated summary is faster than testing, which is why this mistake is easy to make when the build has momentum.

Walk through each expected behavior yourself and inspect the relevant changes. Check failed inputs as well as the happy path. For features involving accounts or private data, review authorization, database writes, and exposed secrets before deploying. Mark an item complete only when the behavior works in the app, not when the tool says it finished.

Keep the spec useful for the next session

Start with the smallest feature you can describe and test. After building it, leave a short note about what works, what changed, and what you will do next. That gives your future prompts a reliable starting point without turning a quick build into a documentation project.

You can keep project decisions in VibeCrumbs while they are still fresh.