Handing Off Vibe-Coded Prototypes to Engineering: What Documentation Actually Needs to Include

Every week, a developer builds a working app in a day using AI. They show it to their team. Everyone’s excited. Then they hand it off to engineering-and everything stops.

The prototype works perfectly in demo mode. But when the engineers try to deploy it, they hit walls: no error handling, no data model docs, no idea why certain code was written the way it was. One engineer spent three weeks reverse-engineering a chatbot just to fix a single edge case. Why? Because the person who built it never wrote down why they asked the AI to do what it did.

This isn’t rare. According to Softr’s 2025 survey of 250 engineering managers, 43% of vibe-coded prototypes stall during handoff because documentation is missing, incomplete, or just plain ignored. The problem isn’t the AI. It’s the assumption that if the code runs, it’s ready.

What Is Vibe Coding, Really?

Vibe coding isn’t magic. It’s using AI tools like Cursor, GitHub Copilot, or ChatGPT to build prototypes by talking to them-like you’re explaining an app to a smart intern. You say: “Make a login screen with Google OAuth and password reset.” The AI spits out working code. In minutes. No typing, no debugging, no staring at syntax errors.

It’s fast. Hexaware’s 2024 report found developers build initial prototypes 70-80% faster using vibe coding than traditional methods. But speed doesn’t equal scalability. The code the AI generates is often a black box. No comments. No context. No traceability.

And that’s the problem. Engineering teams don’t want “it works.” They want to know: Is it safe? Can we maintain it? What happens if the database goes down?

The One Document That Stops Everything From Falling Apart

Before you even type your first prompt, write a Product Requirements Document (PRD). Not after. Not when you’re done. Before.

Aatir Ahmed, who learned this the hard way during his VouchTribe project, says the biggest mistake developers make is starting to vibe code without locking down core requirements. He tried turning a single-user tool into a multi-tenant SaaS halfway through. The code collapsed. Why? Because the foundational assumptions changed mid-build.

Your PRD doesn’t need to be 20 pages. But it must answer:

• Who is this for? (User persona)
• What are the 3 main things it must do? (Core use cases)
• What are the 3 things it absolutely can’t do? (Hard limits)
• What systems does it connect to? (APIs, databases, auth)
• What edge cases matter? (What happens if the user cancels mid-payment? What if the network drops?)

Save this as requirements.md in the project root. Not in a Google Doc. Not in Notion. In the code folder. Engineers will look there first.

Document Every Prompt-Yes, Even the Bad Ones

Engineers don’t care about your creative process. But they need to know why the code looks the way it does.

Every time you prompt the AI to generate a feature, save that prompt. Not just the final one. The failed ones too. Why? Because the AI might have given you three versions of a login flow. You picked one. But why? Was it faster? More secure? Did it use a specific library you’re already licensed for?

Superblocks’ 2025 guidelines say this creates an “audit trail.” That’s corporate speak for: “If this breaks in production, we can trace who said what and why.”

Use a decision-log.md file. Structure it like this:

## Feature: User Authentication
- Prompt: "Create a secure login with email/password and Google OAuth"
- Model used: GPT-4-turbo-2025-01
- Chosen solution: JWT + refresh tokens
- Rejected alternatives: Session cookies (too stateful), OAuth-only (no password fallback)
- Reason: Needed offline access and fallback for users without Google accounts
- Approved by: [Your Name], 2026-01-15

This isn’t busywork. It’s insurance. When an engineer sees this, they don’t have to guess. They know the decision was intentional.

In-Code Comments Are Non-Negotiable

You didn’t write this code line by line. So you didn’t think about every edge case. But the AI did-sometimes.

Hexaware’s 2024 report says: “Because you didn’t write line-by-line, context might get lost.” That’s code for: Your AI-generated function might be doing something clever that only makes sense if you know the prompt.

After every AI-generated block, add a comment. Not “this is a login function.” Something like:

// AI-generated: Uses bcrypt for hashing because it's slow by design to prevent brute force
// Prompt: "Use bcrypt, not SHA-256, for password hashing. Why? Because we need resistance to rainbow table attacks."
// Source: GPT-4-turbo, 2026-01-15
function hashPassword(password) {
  return bcrypt.hashSync(password, 10);
}

Don’t wait until the end to do this. Do it after each generation. It takes 30 seconds. It saves engineers hours.

Git History Must Tell a Story

Engineering teams live in Git. If your commits say “fixed stuff” or “updated UI,” you’ve already lost.

Graphite’s 2024 guide says: “Treat AI-generated code as a pull request you must review and merge carefully.” That means every commit message should explain the why, not just the what.

Bad: feat: login screen

Good: feat(auth): add email/password login with bcrypt hashing + Google OAuth fallback (per PRD v2.1)

And don’t just commit the final version. Commit every iteration. Even the broken ones. Why? Because engineers might need to roll back to a version that worked with an older API. Or they might need to understand how a bug was introduced.

Use branches. Use pull requests. Treat every AI-generated change like you wrote it yourself-because eventually, you’ll be responsible for it.

Security Isn’t an Afterthought

AI doesn’t know your compliance rules. It doesn’t know if you’re under HIPAA or GDPR. It just generates code that works.

Superblocks’ guidelines warn: “AI-generated apps must connect securely to databases, APIs, and legacy platforms.” That means:

• Document every external API key or token used
• Specify which secrets are stored in environment variables vs. hardcoded (hint: none should be hardcoded)
• List all third-party dependencies and their versions
• Explain how data is encrypted at rest and in transit

NIST’s January 2025 update on AI security says: “Lack of data handling documentation is the top cause of compliance failures in AI-generated apps.”

Don’t assume the engineers will figure it out. Write it down. In a security.md file. Include:

• What user data is collected?
• Where is it stored?
• Who has access?
• How is deletion handled?

This isn’t optional. If you’re building for a company with legal teams, this is your ticket to deployment.

Who Should Do This? (Spoiler: Not Just the Builder)

Trying to vibe code, document, and test everything yourself? You’ll burn out.

Teacode.io’s 2025 guide recommends pairing the developer with a PM or designer during prototyping. Why? Because documentation isn’t a technical task-it’s a communication task.

The builder knows the code. The PM knows the user. The designer knows the flow. Together, they can answer: “Is this what the customer actually needs?” and “Will the engineers understand this in six months?”

Split the work:

• Builder: Writes code, saves prompts, adds in-code comments
• PM: Updates requirements.md, writes user flows, defines edge cases
• Designer: Maps out screens and transitions in Figma or a simple flowchart

That’s 15-20% of your prototyping time. For a 16-hour build, that’s 2-3 hours of documentation. That’s the price of a smooth handoff.

Tools Matter-A Lot

Not all AI tools are built the same. Cursor scored 82/100 in Superblocks’ 2025 assessment for documentation quality. ChatGPT? 47/100.

Why? Cursor auto-saves prompts, generates docstrings, and links commits to prompts. Basic tools? You’re on your own.

G2 reviews from Q1 2025 show: 68% of users praised Cursor for “easy handoff to engineering.” Only 31% said the same about basic AI coding tools.

If you’re serious about handoff, use a tool that supports documentation. Don’t rely on ChatGPT alone. It’s a great assistant. Not a production partner.

What Happens When You Skip This?

On Reddit, a developer named CodeArchitect2024 shared how a vibe-coded prototype caused a 3-week delay at a Fortune 500 company. Why? No error handling docs. No data model. No idea what the AI assumed about user roles.

“We had to reverse-engineer the entire flow,” he wrote. “It felt like archaeology.”

Meanwhile, DevLead_SF on Hacker News got their chatbot prototype deployed in 3 days-because they kept a decision log linked to user feedback. The engineering team didn’t have to guess. They had a roadmap.

There’s no middle ground. Either you document-or you delay.

The Future Is Documented

GitHub just launched Copilot Documentation Mode, which auto-generates handoff docs with 85% accuracy. OpenAI released PromptProvenance standards. Gartner predicts that by 2027, 90% of AI dev tools will require documentation for production handoff.

This isn’t a trend. It’s a requirement.

Dr. Lena Rodriguez from MIT says: “Without standardized documentation protocols, vibe coding will remain confined to disposable prototypes.”

That’s the choice you have now: Build a prototype that dies after demo day. Or build one that becomes a real product.

Documentation isn’t the boring part. It’s the bridge.