You've been using Copilot or Cursor for a while now. Sometimes the output is exactly what you needed — in seconds, first try. Other times it produces something plausible-looking that quietly misses the point, and you spend more time untangling it than if you'd written it yourself.
So you try a different phrasing. Switch to a stronger model. Add more inline context. Results improve on one task, then regress on the next. The inconsistency doesn't go away — it just moves.
Most developers conclude that AI coding tools are inherently unpredictable. Some tasks click, others don't, and you never quite know which.
That conclusion is wrong. The inconsistency has a cause, and the cause is almost never the tool.
The real problem is that you're prompting before you've finished thinking.
Why Prompts Alone Aren't Enough
Here's what actually happens when you open Cursor and type a request.
You're translating a half-formed idea into natural language, in real time, under the implicit pressure to keep it short. The AI receives that fragment and tries to infer everything you didn't say: the constraints, the edge cases, the expected output shape, how the code fits the rest of the codebase, what "done" actually means.
When the output is wrong, it's rarely because the model misread your words. It's because the prompt didn't contain enough of the picture. The AI filled the gaps with plausible guesses — and guesses compound.
A better model doesn't fix an incomplete spec. It just hallucinates more confidently.
This is the core failure mode. Developers treat the AI like a search engine — type a query, evaluate the result, refine the query. But AI coding assistants aren't search engines. They're collaborators, and collaborators need context, not just instructions.
Two Developers, Same Task
The difference between a productive AI session and a frustrating one usually comes down to what happened in the 15 minutes before the first prompt.
Consider two developers both building a user notification service.
Developer A opens Cursor and types: "Build a notification service that sends emails and push notifications to users." The AI produces something reasonable — a NotificationService class, some methods, maybe a queue. Developer A integrates it, hits edge cases, goes back to the AI with follow-ups. Each answer patches one gap and opens another. After an hour, the code works, but it's a patchwork held together by iterative prompting, and the architecture reflects that.
Developer B writes a spec first. It takes 15 minutes. Then they paste the spec into a single prompt. The AI's first response covers the priority fallback logic, the retry behaviour, the edge cases — because those were specified. The follow-up prompts are minor. The architecture is coherent. The whole session takes 40 minutes instead of 90, and the output requires half the review time.
Here's what that workflow difference looks like:
The diagram isn't about the AI. It's about what you bring to the session.
What a Spec Actually Is
A spec isn't a PRD. It isn't a design document. It's the structured thinking you were going to do anyway — written down before you start prompting instead of after you start debugging.
Here's the lightweight template that works for most coding tasks:
## Goal
What does this code need to accomplish? One sentence.
## Inputs / Outputs
What goes in? What comes out?
Be explicit about types and shapes.
## Constraints
What must this NOT do?
What dependencies are off-limits?
What are the performance, size, or scope limits?
## Edge Cases
What are the 3–5 scenarios most likely to break
a naive implementation?
## Definition of Done
How will you know this is complete?
Tests? A specific behaviour? Integration with X?
That's it. Five fields. For most tasks, filling this out takes 10–20 minutes. The payoff starts in the first iteration.
How to Fill It In Well
The template is simple. Filling it in well takes a small amount of discipline that most developers skip.
On the Goal field: Write one sentence. If you need two sentences, you have two tasks. Break them apart and spec them separately. AI tools perform better on focused, bounded problems. A vague goal produces a vague result.
On Inputs / Outputs: Be specific about types. "A user object" is not specific. { userId: string, email: string, pushToken: string | null } is specific. The AI will make type decisions either way — you want to make them, not the model.
On Constraints: This is the most underrated field. What you are ruling out shapes the output as much as what you're asking for. "No external queue dependency in v1" is a constraint. "Don't use global state" is a constraint. "Must be testable with a mock transport" is a constraint. These eliminate entire classes of generated code that would have required rework.
On Edge Cases: If you can't name three edge cases, you don't understand the problem well enough to prompt yet. That's not a criticism — it's information. Stop, think for five more minutes, and try again. This is the most valuable thing the spec process forces you to do.
On Definition of Done: This becomes your review checklist. When the AI produces output, you're checking it against a written spec — not a fuzzy mental model. The difference in review speed is significant.
A Filled Example
Going back to the notification service:
## Goal
Send notifications to users via push first, email as fallback.
## Inputs / Outputs
Input: { userId: string, message: string, priority: 'normal' | 'urgent' }
Output: { pushStatus: 'sent' | 'failed' | 'skipped', emailStatus: 'sent' | 'failed' | 'skipped' }
## Constraints
- No external queue in v1 (synchronous is fine)
- Retry once on transient failure, then fail gracefully
- Urgent notifications bypass deduplication
- Service must be injectable with a mock transport for testing
## Edge Cases
1. User has no push token registered
2. Push succeeds but email also fires (should not happen for normal priority)
3. Both channels fail on first attempt — retry behaviour
4. Duplicate notification sent within 5 minutes for same userId + message
## Definition of Done
- Unit tests cover all 4 edge cases
- MockTransport injectable without modifying service
- TypeScript strict mode passes with no errors
Paste that into Cursor. The output you get back will be categorically different from the output you'd get from "build me a notification service."
Why This Feels Slower (But Isn't)
The common objection is that writing a spec takes time you don't have. That feeling is real — but the accounting is wrong.
The 15 minutes spent writing the spec doesn't add to the total time. It replaces time you were going to spend on follow-up prompts, debugging surprising edge case behaviour, reviewing code against a mental model that keeps shifting, and explaining to a colleague what the code is supposed to do.
Spec-driven sessions typically run in one or two meaningful AI turns. Spec-free sessions typically run in five to fifteen turns, with each follow-up increasing the risk of introducing inconsistency into the generated code.
The spec also produces a natural artifact: a document you can commit alongside the code. The next developer — human or AI — reading that code will understand the intent. That's documentation you would have written eventually, written at the moment when it costs least.
The Model Wars Are a Distraction
Every few weeks there's a new benchmark, a new model release, a new claim about which AI coding assistant is definitively better. These conversations consume enormous energy in developer communities.
The uncomfortable truth is that the model choice matters far less than what you bring to it.
A developer who writes a tight spec and uses a model from six months ago will consistently outperform a developer chasing the latest release with vague prompts. The tool is a multiplier. If you bring clarity, you get useful code. If you bring ambiguity, you get plausible-looking noise — at whatever speed the latest model runs.
The spec is the skill. The AI is the accelerator.
This doesn't mean model choice is irrelevant. For complex reasoning tasks, architectural decisions, or debugging subtle bugs, a stronger model genuinely helps. But for the bread-and-butter tasks that make up most of a developer's day — building a service, writing tests, implementing a feature — the spec matters more.
What Changes When Your Team Adopts This
The impact of spec-driven AI development isn't just individual. When a team adopts it, several things shift at the process level.
Code reviews get faster. Reviewers aren't trying to infer intent from generated code. The spec is the stated intent — they're checking the code against it.
Onboarding gets easier. New team members can read a spec alongside the code and understand not just what it does but why it was built the way it was.
AI-assisted PRs become trustworthy. The main anxiety about AI-generated code in production is the question "did anyone actually think through this?" A committed spec answers that question.
Junior developers level up faster. Writing edge cases before prompting forces the kind of systematic problem decomposition that used to come only from years of experience. The spec process teaches it explicitly.
The Broader Principle
Spec-driven development predates AI by decades. The insight that you should know what you're building before you build it is not new.
What's changed is the leverage. A clear spec used to unlock a slightly faster build. Now it unlocks a dramatically faster one. The AI amplifies the quality of the input. Fuzzy thinking gets amplified into a large volume of fuzzy code. Clear thinking gets amplified into a large volume of usable code.
The developers who will get the most out of AI tools in the years ahead are not the ones who write the cleverest prompts. They're the ones who think most clearly before they type anything.
The question isn't which AI tool you should use. It's whether you're giving any of them a fair shot.
Actionable Takeaways
- Before your next AI-assisted task, spend 10 minutes filling in the five-field spec template. Don't skip edge cases — that's where the value is.
- If you can't write a "Definition of Done", stop prompting. You're not ready yet.
- Commit the spec alongside the code. Treat it as a first-class artifact, not a scratchpad.
- Use the spec as your review checklist. If the generated code doesn't satisfy every field, the session isn't done.
- Apply this to one task this week and count the AI turns compared to your usual workflow. The difference is immediate and measurable.
The tools are good. Give them something good to work with.
