From Prompt to Production: A Developer Workflow for Building with AI

When I first started using AI, I was stuck thinking small.

A prompt here to name a function.
A suggestion there to refactor some code.
An autocomplete from Copilot that's 70% right but needs tweaking.
Maybe a regex from ChatGPT that works on your test case but breaks in production.

Useful? Sometimes.
Repeatable? Rarely.
Strategic? Not at all.

This is what I call piecemeal AI: isolated, ad-hoc, disconnected tasks that feel helpful in the moment but don’t build toward anything bigger. It’s like asking a senior engineer for one-off help on ten different tickets — without ever learning how they think.

I know this pattern well. I lived in it for months.

I was testing every new tool. Saving cool prompts. Getting occasional velocity bumps. But I still didn’t know:

• When should I use AI vs just write it myself?

• How do I debug model failures systematically?

• What does “good” prompting even look like in a real dev loop?

Eventually, I realized the real problem:

AI wasn’t integrated into my workflow — it was duct-taped to it.

And that’s why progress stalled. Not because I didn’t know how to prompt. But because I didn’t have a system.

The Breakthrough: Systematize the Stack, Not Just the Prompt

Everything changed when I stopped asking “What’s the right prompt?”
…and started asking:
“What’s the right system?”

That shift led to a simple but powerful insight:

You don’t scale by prompting harder. You scale by prompting smarter — inside a repeatable workflow.

I stopped chasing one-off AI tricks and started designing an AI-native stack around how I actually build:

• I created prompt templates tied to specific dev tasks

• I logged what worked, what failed, and why

• I added guardrails and evals to catch bad generations early

• I used AI to scaffold features, write tests, and prep docs — all in one loop

The results weren’t just faster. They were clearer, more repeatable, and easier to improve over time.

Instead of treating AI as a shortcut, I started treating it like infrastructure.

And that shift unlocked something bigger: a consistent, scalable Prompt-to-Production pipeline — one that uses AI at every stage of development, without breaking trust or flow.

Systematizing AI is about building feedback loops that get smarter over time. Most developers quit before reaching this point because the first 10 attempts feel like more work, not less. They're right — it IS more work upfront. The payoff comes after prompt #50, when your personalized patterns start compounding.

The Framework: 6 Stages from Prompt to Production

In the early days, I used AI like duct tape — patching one task at a time.

But compound benefits comes from treating AI like a workflow engine.

That’s where the Prompt-to-Production framework comes in — a 6-stage developer loop designed to turn vague ideas into shipped features with strategic AI assistance.

Here’s the high-level map:

Each stage maps to a real dev task. Each one can be AI-assisted, AI-augmented, or fully AI-native.

It’s about designing a workflow where AI is embedded intelligently, not bolted on.

Realistic Timeline:

• Stages 1-2: You'll nail these in week 1

• Stage 3: Takes 2-3 weeks to feel natural

• Stage 4: The hardest stage — expect 4-6 weeks of iteration

• Stages 5-6: Often skipped but provide 40% of the value

• Full loop mastery: 2-3 months of daily practice

Most developers give up at Stage 4. That's exactly where the leverage begins.

Coming up next:
We’ll break down each stage — with examples, prompts, and pitfalls to avoid.

Stage 1: Frame the Feature

Most devs skip this step.
They drop vague specs into GPT and hope for the best.

The result?
Bloated outputs. Confused flows. Code that technically works — but doesn’t solve the real problem.

Framing the feature means taking a breath and asking:

• What am I actually building?

• Who’s it for?

• What edge cases matter?

• What does success look like?

This step creates a tight prompt boundary — so you can ship smarter, not sloppier.

Prompts That Help

Instead of:

Try:

This creates:

• Clarifying questions

• Design constraints

• Implementation guardrails

It also gives you reusable scaffolding for future features — prompt fragments like:

AI-Native Tips

• Systematize your prompts: Build a feature briefing template.

• Loop in stakeholders: Use AI to simulate PM or designer feedback before you code.

• Use diagrams: Tools like Mermaid + AI can co-generate flowcharts from specs.

Common Mistakes

• Starting with “Write a component for X” before defining what X actually is

• Leaving key behaviors, edge cases, or constraints undefined

• Not reusing successful prompt scaffolds for similar tasks

The Overspecification Trap

Don't confuse thorough framing with writing a novel. If your prompt is over 500 words, you're probably mixing framing with implementation.

Good frame: "Multi-step login with email/password and optional TOTP 2FA"
Too much: [500 words describing every button state and error message]

Stage 2: Forge the Foundation

Once your feature is framed, avoid jumping straight into code generation.
Forge the foundation first — layouts, logic flows, component trees, file structure.

Why?
Because large language models are great at shaping patterns — but only when you give them a structure to fill.

This is where AI becomes a more capable assistant.

This is not the stage for implementation details, UI polish, or deep logic.
It’s the phase where you turn a vague feature into a clearly structured blueprint.

Prompts That Actually Work

❌ Weak: "Create a login component"

✅ Better: "Outline the component structure for a login flow"

🎯 Best: "Design the folder structure and component hierarchy for:
- Parent: LoginFlow component
- Children: EmailStep, PasswordStep, TwoFactorStep
- Shared: ValidationHelpers, AuthContext
- Output format: Tree structure with prop flow indicators"

The final prompt produces:

LoginFlow/
├── index.tsx (orchestrator)
├── steps/
│   ├── EmailStep.tsx     → onValidate(email) → parent
│   ├── PasswordStep.tsx  → onSubmit(creds) → parent
│   └── TwoFactorStep.tsx → onVerify(token) → parent
└── shared/
├── ValidationHelpers.ts
└── AuthContext.tsx    ← provides: {user, login, logout}

You’re not asking for final code — you’re building the skeleton.

AI-Native Tips

• Use Markdown + AI: Let the model output a full structure you can paste and fill.

• Pair with visual tools: Copilot Workspace or Cursor’s tree view helps visualize what you’re building.

• Version your scaffolds: Save “v1 scaffolds” so you can trace prompt drift or debug upstream issues later.

Common Mistakes

• Asking for fully coded features before outlining their architecture

• Mixing too many concerns (logic, styles, UX) into one prompt

• Ignoring modularity — which limits reuse across features

Stage 3: Fortify with Tests

Before you generate the implementation, generate the tests.

It feels backwards — but it’s one of the most underrated AI-native upgrades.

Why?
Because AI thrives on clear specs and examples. And tests are the clearest spec you can give.

Instead of writing tests after code, use AI to define expected behaviors first, which then shape better implementation.

Prompts That Help

Instead of:

Try:

You can then feed the generated tests back to the model to write compliant code.

This tightens the loop:

• Frame the spec

• Generate tests

• Generate code that passes them

And because LLMs are better at reasoning from examples, the tests often improve the final code’s quality.

AI-Native Tips

• Prompt with edge cases: AI tends to miss subtle failure modes unless told.

• Log tests like you log code: They become reusable patterns for future features.

• Automate evals: Use tools like Promptfoo to auto-run your generated tests across prompt variants.

Common Mistakes

• Writing tests after generation — reinforcing bugs instead of preventing them

• Leaving test generation entirely manual

• Using vague test prompts that don’t capture business logic

⚠️ The Test Generation Paradox

AI is excellent at generating happy-path tests but terrible at imagining what could go wrong. Use this hybrid approach:

1. AI generates: Basic test structure and happy paths

2. You add: Business logic edge cases AI would never think of

3. AI expands: More variations once you've shown the patterns

Example:

• AI will test: "User enters valid email"

• AI won't test: "User rage-clicks submit 10 times causing race conditions"

• You teach it once, it remembers forever

Stage 4: Fine-Tune the Flow

You can think of scaffolding (Stage 2) as the skeleton, and test writing (Stage 3) as the guardrails. The actual code that fills it all in? That’s Stage 4.

Once your structure and tests are in place, it’s time to generate working code.
But don’t treat it as a one-shot task — treat it as a conversation.

This is where most AI-assisted devs get stuck:
They prompt for an implementation, get flawed output, and give up or manually patch it.

AI-native devs?
They guide the model through a feedback loop.

You’re not just accepting what it gives — you’re shaping it, step by step.

The Conversation Pattern That Works

Round 1: "Implement the login form based on these tests [paste tests]" → AI gives you 80% working code

Round 2: "The validation triggers on every keystroke. Make it trigger on blur" → AI fixes the specific issue

Round 3: "Add loading states during authentication" → AI enhances without breaking existing code

Round 4: "Refactor to use our company's Button component from @/components/ui" → AI adapts to your codebase patterns

Never ask for everything at once. Layer your requirements like paint.

AI-Native Tips

• Paste in tests or expected outputs: Context helps the model self-correct.

• Log failed attempts: They often reveal reusable fixes or bad patterns.

• Iterate intentionally: Instead of regenerating the whole file, patch specific logic chunks.

Think of this as pair programming — but you’re the lead, and the AI’s here to iterate fast.

Common Mistakes

• Treating generation like a vending machine (prompt → code → done)

• Skipping test-driven iteration

• Not saving intermediate versions (you’ll lose your best ideas)

Stage 5: Finalize + Ship

Once the implementation passes tests, your job isn’t over.
You need to prepare it for handoff, review, or deployment — and AI can help here too.

This stage is about wrapping the work:
Docs, PR descriptions, commit messages, changelogs, release notes — anything that explains why it matters and how it works.

When you prompt for finalization, you're cleaning up code while increasing team velocity.

Prompts That Help

Instead of:

Try:

This helps the AI shift into explainer mode — and you move from builder to communicator.

AI-Native Tips

• Prompt for multiple artifacts: One code change can generate a PR body, inline comments, and docs in one go.

• Refine tone: Prompt again if the style isn’t teammate-friendly. Use your past PRs as examples.

• Save doc prompts: You’ll reuse them more than you think — especially if you commit regularly.

The 5-Minute Rule

If your finalization takes more than 5 minutes, you're doing it manually. Set up these templates once:

.ai-templates/ 
├── pr-description.md 
├── commit-message.txt 
├── changelog-entry.md 
└── review-checklist.md

Then every ship becomes: "Generate PR description using our template for [feature-type] changes"

Common Mistakes

• Rushing to merge without summarizing the intent

• Writing documentation manually when AI could draft it measurably more effectively

• Forgetting to explain context — which slows down onboarding and reviews

Stage 6: Feedback & Learn

Most developers never reach this stage. Here's what they're missing:

The Weekly Prompt Review (10 minutes):
1. Export your last week's AI conversations
2. Find the prompt that took the most iterations to get right
3. Rewrite it with what you learned
4. Save it to your prompt library with tags

The Failure Log (2 minutes per incident):

When AI generates broken code, don't just fix it. Log:
- What you asked for
- What it gave you
- Why it failed
- The prompt that would have worked

After 20 failures, you'll see patterns:
- "AI always forgets error boundaries" → Add to your base prompt
- "It uses deprecated APIs" → Include version constraints
- "It overwrites my custom logic" → Use more specific selectors

The Compound Effect:
Week 1: 10 attempts to get working code
Week 4: 3 attempts for the same result
Week 12: First attempt usually works
Week 24: You're teaching others your patterns

That’s it — the full Prompt-to-Production loop.

❌ Before: Disconnected, Low-Leverage

1. Prompt GPT to scaffold a component.

2. Copy it into your IDE.

3. Hit an error → try a different prompt.

4. Tweak the code manually.

5. Repeat for tests, docs, and PR messages — from scratch every time.

It’s reactive, inefficient, and hard to scale. Every task is a one-off. There’s no feedback loop. No patterns. No evolution.

✅ After: Integrated, High-ROI

Using the Prompt-to-Production framework:

1. Frame the feature clearly — you and the model start on the same page.

2. Forge reusable scaffolds: folder layout, prop flows, component trees.

3. Fortify with tests before implementation — specs drive code.

4. Fine-Tune the feature with structured prompts (e.g., generate edge case handling).

5. Finalize + Ship with consistent PR messages, commit summaries, and docs.

6. Feedback & Learn — log what worked, version prompts, and track success.

Every step reinforces the next. Prompts become assets. Output becomes input. You’re engineering with AI.

Prompt Patterns for Each Stage

Across the six stages, different styles of prompting work best.

This section shows you how to structure your prompts — whether you’re using GPT-4, Claude, Cursor, or anything else.

Stage-by-Stage Prompt Patterns

When to Reuse, Adapt, or Retire

• Reuse prompts that produce clean, modular outputs — these become templates.

• Adapt prompts as your features evolve — reuse the pattern, not the wording.

• Retire prompts when they start producing stale or hallucinated results — especially after model upgrades.

The Reality Check: Where This Actually Breaks

Let's be honest about where this framework struggles:

🔴 Legacy Codebases: If your codebase is 10 years old with custom everything, AI will generate modern patterns that don't fit. Start with Stage 6 (learning your patterns) before attempting Stages 1-5.

🔴 Complex Business Logic: AI can't scaffold what it doesn't understand. If your feature involves regulatory compliance or domain-specific rules, you need to frame 10x more carefully.

🔴 Team Resistance: If your team isn't bought in, your beautiful AI-generated code will get rejected in PR reviews. Start with Stage 5 (documentation) to build trust.

🔴 The Context Ceiling: After ~50 prompts in one session, AI starts forgetting your context. You need to reset and re-establish patterns regularly.

These aren't reasons to avoid the framework — they're reasons to adapt it.

The Hidden Costs Nobody Talks About

Token Burnout: At $0.01-0.03 per 1K tokens, a single feature can cost $5-20 in API calls during development. Your monthly bill might surprise you. Budget $200-500/month per developer for serious AI-native development.

The Review Bottleneck: AI can generate 1000 lines in 30 seconds. Your senior engineer needs 30 minutes to review it properly. You've just moved the bottleneck, not removed it.

Context Switching Tax: Jumping between your IDE and AI chat breaks flow state. Each context switch costs 15-25 minutes of deep focus. Until you master integrated tools, you might actually be slower.

The Debugging Nightmare: When AI-generated code fails in production at 3am, you're debugging code you didn't write, with patterns you didn't choose, in a style you might not prefer. Hope you logged those prompts.

If You're Already Using AI (But It's Not Working)

Symptom: "We prompt 10 times to get decent code"
Fix: You're missing Stage 1 (Frame). Spend 2 minutes on requirements, save 20 minutes on regeneration.

Symptom: "AI code doesn't fit our codebase"
Fix: You need Stage 6 (Learn) first. Mine your best code for patterns, then teach them to AI.

Symptom: "Different devs get different results"
Fix: You lack shared context. Create a team prompt library. Start with 5 patterns everyone uses.

Symptom: "It works in ChatGPT but breaks in production"
Fix: You're skipping Stage 3 (Tests). AI lies confidently. Tests don't.

Prompt-to-Production FAQ’s

Not every dev is building end-to-end features solo. Not everyone is using ChatGPT. Some aren’t ready for agents yet. That’s the point of this framework — it scales to your stack, team role, and tool of choice.

Here’s how it holds up under real-world conditions:

“I don’t build big features solo.”

Even if you’re just writing unit tests, reviewing PRs, or building small components — you can apply the relevant stages to your slice of the work.

In fact, having a shared mental model like this improves handoffs, makes your prompts more reusable, and creates smoother team interoperability.

“I just need help with X — not a whole system.”

That’s fine.

You don’t have to use all 6 stages from day one. Start with the pain point.

• Need better tests? Focus on Stage 3.

• Struggling with boilerplate? Try Stages 2 and 4.

• Writing docs no one reads? Stage 6 helps turn them into real-time feedback loops.

The stages are modular. Use what’s useful.

“Does this even work without agents?”

Yes, and honestly, it works BETTER without agents for your first 3 months. Agents add complexity before you've mastered the basics. It's like learning to code by starting with microservices — technically possible but practically painful.

Master the manual loop first:
- Month 1-3: Copy-paste between your IDE and ChatGPT
- Month 4-6: Graduate to Cursor/Copilot integration
- Month 7+: Consider agents for repetitive tasks

The developers who succeed with agents already have strong prompt patterns. The ones who fail jumped straight to automation without understanding what they're automating.

“I don’t use ChatGPT — does this still apply?”

Yes.

The point isn’t which tool you use — it’s how you use it. This framework is tool-agnostic by design.

If your prompts are structured, versioned, and logged, they’ll work across GPT-4, Claude, Gemini, or whatever model comes next.

Prompt portability is real. And when you treat your prompts as reusable assets, it’s easier to adapt across tools.

Your Next Steps: Start with One Loop

You don’t need to overhaul your whole dev process tomorrow.

The easiest way to go AI-native?

Run a single feature through all six stages.

• Frame it clearly

• Scaffold the structure

• Write the tests first

• Generate the code

• Ship with confidence

• Capture learnings

That’s one loop.

Do this once, and you’ll feel the shift: less context-switching, more clarity, better output.

Repeat it a few times, and you’ve built a system.

Start Your Prompt Log

Don’t just prompt and forget.

Create a log where you:

• Save high-performing prompts

• Track test results

• Spot reusable patterns

• Tag by task, tool, and failure mode

You’ll build a personal library of assets that compounds over time.

Use my free Prompt Log Template to get started — or create your own system in Notion, Obsidian, or Markdown.

Not Everything Has to Be AI-Generated

AI-native doesn’t mean AI-only.

It means building workflows where:

• Prompts are versioned like code

• Feedback loops are baked in

• Your tools learn from your own dev habits

Start small. Improve fast.

Want the Full Checklist?

This post walked you through the Prompt-to-Production Framework.

But if you’re the kind of dev who wants zero-fluff, actionable tools to integrate this into your real workflow…

Grab the free Prompt-to-Production PR Checklist:

• Covers all 6 stages of the Prompt-to-Production loop

• Designed for real teams shipping real code — not toy demos

• Works across frontend, backend, and integration workflows

• Includes prompt examples, QA checks, edge case reminders, and full-stack considerations

• Helps you structure better PRs, write clearer specs, and level up your test game

Built by a dev (me) using this system in production — and improving it with every cycle.

🔗 Download the Checklist

Or subscribe to get future frameworks delivered straight to your inbox:
👉 Prompt/Deploy Newsletter