Vibe Thinking - The PM Who Writes Requirements That an AI Can Actually Use

The dev team is moving fast. Requirements come in, developers build quickly, and then the demo happens.

The outcome isn't what was wanted. The brief was technically correct but the result was wrong. The team assumed shared context that was never written down. Nobody asks for specifications now; you just prompt the AI and go. And that's exactly the problem. Every org I've spoken with that has rolled out AI coding tools has had some version of this moment.

Vibe coding built the wrong thing fast.

The error happened upstream in the brief. Vibe coding didn't create that problem; it made the same old problem arrive faster.

This is Post 2 in the Vibe Thinking series ↗. Post 1 covered the developer layer ↗ - what changes, what doesn't, and why the review burden goes up when output volume triples. This post is about what happens upstream of that: what the developer receives before they open the AI agent.

The Upstream Problem

Every productivity gain vibe coding delivers is conditional. It conditions on what goes in.

When a developer prompts an AI coding agent, they're working with the context available to them. That context comes from two places: their knowledge of the system, and the requirements they were handed. If both are precise, the output is usually good. If either is ambiguous, the AI fills the gap - and it fills it with what's plausible, not what was intended.

The AI doesn't ask clarifying questions. It makes assumptions at speed.

This is the upstream problem. Requirements have always mattered. But when code moved slowly, ambiguity had friction - a developer would pause, think it through, maybe fire off a Slack message. That friction was invisible quality control. In a vibe coding workflow, that friction is gone. The assumption gets built in milliseconds.

Better tools amplify whatever quality goes into them. A vague brief produced one kind of wrong answer before. Now it produces the same wrong answer, delivered in a fraction of the time.

What "AI-Ready" Requirements Actually Mean

"AI-ready" is a precision threshold, not a new format or template.

A traditional requirement leaves room for interpretation. That was acceptable - sometimes even desirable - when human developers read it. Developers bring domain knowledge, ask questions, check back with the PM. They use judgment to fill gaps.

A prompt works differently. When a developer writes a prompt based on a requirement, they're compressing the brief into instructions for a system that will execute exactly what it receives, filling every ambiguous corner with inference.

Precision means less interpretation space, not more words.

An AI-ready requirement answers: what is the outcome? What is in scope and explicitly not in scope? What does success look like, observable and testable? What edge cases need to be handled - and which ones don't? That's it. No padding, no context assumed, no "dev will figure it out."

The difference between a human-readable requirement and a machine-precision one is the number of valid interpretations. A human-readable requirement might have five; an AI-ready one has one.

Why User Stories Aren't Enough Anymore

User stories were designed for human interpretation.

"As a user, I want to filter my results so that I can find what I'm looking for." That sentence works in a room where a developer, a designer, and a QA engineer can all read it and then fill in the gaps through conversation. It was never a specification. It was always a starting point.

In a vibe coding workflow, that story becomes the input to a prompt. And now all the gaps - filter by what? Which results? Which fields? Default state? Empty state? Error state? - get filled by AI inference, not by a twenty-minute conversation. The gaps remain. They just close differently.

User stories written for human conversation don't survive contact with AI execution.

Acceptance criteria help, but they help only if they're written with precision rather than breadth. The difference is stark:

The first version has at least six valid interpretations. The second has one. That's the difference a vibe coding workflow actually needs.

The shift is completing user stories, turning the starting point into a closed specification before it reaches the developer's machine.

What to Let Go Of

This is about changing what the PM's output actually is, not working harder or producing more documentation.

Requirements as ongoing conversation. The pattern of handing over a rough brief and expecting the developer to come back with clarifying questions doesn't work when the developer's first move is to open an AI agent. By the time they come back, they've already built something. The clarification moment is gone.

Thin work items. A ticket that says "Add export functionality" is an instruction, not a specification. It invites interpretation. In a vibe coding workflow, that interpretation becomes code within minutes. Thin work items produce thin outcomes.

Big upfront handover specs. Writing a 30-page spec once, handing it over at the start of a quarter, and waiting for the end of the sprint is the opposite of the pipeline model. Vibe coding needs a continuous feed of small, precise, ready-to-build items - not a large document that's already out of date by the time the first sprint ends.

Waiting for the current sprint to finish. If the developer is building in hours what used to take days, the backlog dries up faster than before. A PM who starts preparing the next batch when this sprint closes is already too slow. The queue needs to stay ahead of the team, not catch up to it.

The PM as backlog manager. Managing a backlog is an operational activity. Running ahead of the dev team as an outcome definer and ambiguity eliminator is a strategic one. The role has to shift from maintaining the list to feeding the pipeline.

Clarification meetings. If something needs a meeting to be understood, it wasn't written clearly enough. Async clarification via a shared doc is acceptable. A scheduled thirty-minute call to explain what a ticket means is a symptom, not a process.

The Atomic Feature Brief

The unit of PM output in a vibe thinking org is the atomic feature brief.

Not a user story. Not a ticket title. Not a paragraph in a spec doc. A self-contained, closed specification that a developer can hand directly to an AI agent without losing anything in translation.

A well-written atomic feature brief answers four things:

1. Outcome. What is the end state the user reaches? Describe it from the user's perspective, in observable terms - not "we add a filter" but "the user can filter the results list by date range, category, and status, and the filtered view persists across navigation."

2. Scope boundary. What is explicitly not in this atomic feature? If sorting is not included, say so. If mobile layout will be handled separately, say so. Without scope boundaries, vibe coding over-builds. The AI doesn't know where to stop.

3. Acceptance signal. How does the developer know this is done? Not "QA approves it" - that's a process gate, not a signal. The acceptance signal is a specific, observable condition: "The filter updates results without page reload, preserves selection state, and shows the total result count with active filters applied."

4. Explicit edge cases. What happens at the boundaries? Empty state. Error state. Maximum values. Concurrent use. Offline. Every gap the developer doesn't receive in writing is a gap the AI will fill with inference.

The test of a good atomic feature brief: read it back. Does it have exactly one valid interpretation? If you can imagine two teams building two different things and both claiming they followed the brief, it isn't ready yet.

Here's what a complete atomic feature brief looks like in practice - taking "Add export functionality" (a thin ticket) through all four components:

This brief can be handed directly to a developer, who hands it directly to an AI agent. Nothing is left to inference. That's the standard.

The Pipeline Model

A vibe thinking PM operates ahead of the dev team, not alongside them.

The pipeline model is simple: at any point in time, there is a ready queue of atomic feature briefs that have been written, reviewed for precision, and confirmed scope-complete. When a developer finishes one, they pick up the next without waiting for the PM to write it.

This is a shift in planning rhythm. Preparation doesn't happen at the start of a sprint - it happens continuously, one or two iterations ahead of wherever the dev team currently is. The PM's planning cycle is shorter and faster. Atomic features are written, reviewed, and queued before they're needed, not when they're needed.

The backlog is the fuel that keeps the team moving; an empty backlog leaves a high-performance engine sitting idle.

The practical implication: PMs and BAs need time carved out specifically for brief-writing. Not sprint ceremonies, not backlog grooming sessions where work is triaged in bulk - dedicated time for writing precise, closed specifications that are ready to build the moment they're picked up.

AI-Powered Requirement Writing

Here's an irony worth naming: the same AI that accelerates development can also accelerate the quality of requirements. And most PM teams haven't tried it.

Writing an atomic feature brief and then asking an AI to identify ambiguity, surface missing edge cases, or generate acceptance criteria is a legitimate practice - and it changes the output. AI finds the gaps that a second human read would miss, and it does so immediately.

Concretely, this looks like:

This is about using AI as a precision auditor on your own work before it reaches the developer. Most PM teams I've spoken with haven't tried this; the ones that have describe it as the fastest way to find their own assumptions. A brief that has survived AI interrogation is already a better input than most tickets on most backlogs today.

The PM still owns outcome definition. AI helps complete the specification.

The Precision-Security Link

Ambiguous requirements aren't just a quality risk. They're a security risk.

When a brief leaves scope undefined, the developer has to fill the gap. In a vibe coding workflow, they fill it via prompt. And when a prompt lacks scope constraints, the AI fills the remaining gap with what's plausible - which often means functional patterns that haven't been evaluated for security, data handling, or access control.

Interpretation space in a brief becomes improvisation space in the code.

A brief that doesn't specify authentication requirements for a new endpoint leaves the developer to assume. The AI builds something that works. It may not validate session state. It may not scope the data returned to the requesting user. It may not sanitise inputs. These are the predictable output of an AI working from an incomplete specification.

45% - fail OWASP security tests
The Veracode 2025 GenAI Code Security Report ↗ found that 45% of AI-generated code samples across Java, JavaScript, Python, and C# failed security tests and introduced OWASP Top 10 vulnerabilities. That failure rate correlates directly with the quality of the context the AI was given.

Precise requirements reduce the surface area for AI to improvise. When scope is explicit, authentication expectations are stated, and data handling is specified upfront, the developer's prompt carries that precision - and the output is more constrained and more auditable. PM craft is directly connected to downstream OWASP compliance, and that connection is causal, not metaphorical.

The New PM Role

The PM in a vibe thinking org is an outcome owner who runs ahead of the team.

Not a backlog curator or ceremony facilitator. An outcome definer who provides a continuous stream of precision specifications that the development pipeline can consume without interruption.

The role is upstream. The value is in the quality of what enters the pipeline.

This means the PM's most important skill set shifts. Domain knowledge still matters - you can't define outcomes without understanding what the product is trying to do. But the differentiating capability is now precision writing: the ability to close a specification completely before it moves downstream.

It also means the PM has a stake in the security and quality posture of the product. Not because they review code - they don't - but because their requirements shape what the AI builds and what constraints it operates within. A PM who understands that ambiguity creates security risk writes differently than one who doesn't.

Most PMs already know they can write better requirements. The question is whether the organisation actually gives them the time and the mandate to do it.

Working With Flytebit

At FLYTEBIT TECHNOLOGIES, Vibe Coding Transformation ↗ is a structured engagement - not a workshop and a tool licence.

Requirements quality is a process and skills problem - not a tool problem. The requirements layer is part of every transformation engagement we run, because it's where the most sprint time is actually lost, and it's the part most organisations skip when they roll out AI coding tools. They train the developers and leave the PM workflow unchanged.

We work with PM and BA teams to map the current requirements process, identify where interpretation gaps are introduced, and establish the atomic feature brief model as a working practice - so the shift from concept to practice happens with support, not just a framework document.

Two of our products address the quality layer that sits directly downstream of requirements.

DOCKR ↗ is an AI-powered documentation automation platform built for codebases that move fast. Connect your repository once - DOCKR analyses your code and auto-generates living documentation: architecture diagrams, component maps, and dependency graphs, refreshing automatically on every push via webhook. No developer effort after setup; documentation stays current and accurate across 11 programming languages without anyone having to write it. Know more ↗

PASSR ↗ intercepts every PR and commit automatically - reviewing across eight dimensions (Performance, Availability, Security, Scalability, Architecture, Error Handling, Code Quality, Testing) and delivering each finding with a description, impact, and ready-to-apply fix before any human sees the diff. Merge protection blocks critical PRs; the portal tracks quality trends across all repos. Know more ↗

Not sure where your organisation stands today? The Vibe Coding Transformation Readiness Quick Check ↗ takes five minutes and gives you a per-function view of where your pipeline is most exposed.

If your organisation is investing in vibe coding and hasn't yet looked at the requirements layer, that's the conversation to start ↗.

What's in This Series

This is a six-part series. Each post is written for a specific function - mapping what has to change, what has to go, and what the new version of that role looks like in a vibe thinking org.

POST 0 — Everyone — The Full Org Transformation ↗ - Why developer-only vibe coding doesn’t change the sprint — and what the full-org transformation actually requires.

POST 1— Developers — The Developer Who Codes at the Speed of Thought ↗ - The craft shifts. The hours don’t. What actually changes in a developer’s day — and the discipline required to make it safe.

← You are here — POST 2 — PMs & BAs — The PM Who Writes Requirements That an AI Can Actually Use - Faster code built from vague briefs is just faster garbage. What AI-ready requirements look like — and why ambiguity is now a security risk.

POST 3 — QA & DevOps — When QA Becomes the New Bottleneck - When code ships in hours and testing still takes days, you’ve just moved the queue. How QA has to evolve to keep pace.

POST 4 — Tech Leads — The Team Lead Who Stopped Managing and Started Building Again - Senior engineers stuck in coordination roles can’t vibe code. What it looks like when tech leadership gets back to building.

POST 5 — Leadership — The Org That Rewired Itself to Ship Faster - What the org looks like when every layer has made the shift — the metrics, the failure modes, and what to stop measuring.

Key Takeaways

✅ Vibe coding amplifies whatever quality goes in: Ambiguous requirements produce wrong outcomes faster, not better ones. The error happens upstream of the code.
✅ User stories written for human conversation don't survive AI execution: They need to be closed specifications with explicit scope, outcomes, and edge cases before they reach the developer.
✅ The atomic feature brief is the PM's primary output: Outcome, scope boundary, acceptance signal, explicit edge cases - no interpretation space. One valid interpretation.
✅ PMs need to operate ahead of the dev team, not alongside them: A continuous, ready queue of atomic features is the fuel that keeps a vibe coding pipeline moving. An empty backlog idles the engine.
✅ AI can audit requirement precision before work is handed off: Prompting for ambiguity, missing edge cases, and acceptance criteria is a legitimate PM practice - and it changes the output.
✅ Ambiguous requirements are a security risk: Interpretation space in a brief becomes improvisation space in the code. PM craft is directly connected to downstream OWASP compliance.
✅ The PM role in a vibe thinking org is outcome owner and pipeline feeder: Not backlog manager, not ceremony facilitator. The value is in the quality of what enters the pipeline.
✅ Organisations that invest in AI coding tools without rethinking the requirements layer are optimising the wrong constraint: Developer velocity without PM precision produces fast garbage, not fast value.