If you're asking whether specs are better than PRDs, you're asking the wrong question.
For AI-driven development, they do different jobs. A PRD answers what should we build and why for humans. A spec answers what will the AI agent execute. Mature teams use both. Solo builders usually need less PRD and more spec.
That also explains the backlash. People call spec-driven development “waterfall with markdown,” “spec drift,” or a token-burning ceremony because they're often using one bloated document for every job. That fails. In a real build loop, the document for human alignment and the document for machine execution shouldn't be the same thing. Even the discussion on r/ChatGPTCoding about specification being an overloaded term points at the same mess. People are arguing about one word that now covers several very different artifacts.
The useful question is simpler. Which document helps you ship correct code with the fewest mistakes?
Table of Contents
- PRDs and Specs Arent Enemies They Do Different Jobs
- When to Write a Product Requirements Document
- When to Write a Technical Spec
- PRD vs Spec A Direct Comparison for AI Teams
- How to Avoid the Spec-Driven Development Traps
- Choosing Your Workflow With AI Agents
PRDs and Specs Arent Enemies They Do Different Jobs
The cleanest way to think about this is job-to-be-done.
A PRD is a coordination doc. A spec is an execution doc. If you blur them together, you get a document that's too vague for Cursor or Claude Code and too technical for a co-founder or marketer.
The real split
Historically, software teams didn't stop using PRDs. They split what and why from how as systems got more complex. Martin Fowler's functional spec writing and later Productboard guidance both draw that line clearly. Productboard explicitly recommends using both documents when work has real implementation complexity, with the PRD for alignment and the spec for technical depth and implementation detail, as summarized in this breakdown of functional specs and the PRD-spec split.
Practical rule: If a human needs to agree on the problem, write a PRD. If an agent needs to change files correctly, write a spec.
Why founders get confused
Most advice comes from enterprise product teams. You don't have that setup. You may not have design, product, and engineering all arguing in a meeting. You have a repo, an AI agent, and a feature that needs to work.
That changes the answer. For a solo builder, a PRD often becomes optional. A spec usually doesn't.
When to Write a Product Requirements Document
Write a PRD when you need to align humans around the problem, not when you need an agent to write code.
If you're working with a co-founder, designer, contractor, or even your future self on a messy product bet, a PRD helps. It captures the user problem, constraints, expected outcome, and tradeoffs. It should stay high level. Once you start stuffing it with endpoint details, data models, and edge-case UI states, it stops being a PRD and turns into a bad spec.
Good use cases for a PRD
- New product bets: You're still deciding what matters.
- Cross-functional work: Other humans need context, not implementation detail.
- Decision records: You want to remember why you picked this path.
A lightweight doc in Notion is enough for most founders. If you're deciding where to keep that kind of planning, this review of features of Notion vs Confluence is useful because it shows the tradeoff between lightweight collaboration and heavier documentation systems.
A PRD should help you decide. It shouldn't pretend to be code.
If you need a starting point, use a lean format like this PRD guide for founders. Keep it short. If you're building alone and already know the problem, don't write a six-page PRD out of habit.
When to Write a Technical Spec
Write a spec when the next step is implementation.
A useful spec is grounded in the codebase. It names files, interfaces, scope boundaries, failure cases, and acceptance criteria. It tells the agent what to touch, what not to touch, and how you'll know the work is done.
For AI work, this matters even more because the strongest shift is toward executable acceptance criteria. Braintrust makes the point directly in their argument that evals become the spec. An eval suite can run on every commit and show whether the product is improving. A PRD can't do that.
What belongs in the spec
- Context: What feature is changing and why it exists
- Scope boundaries: Especially what you are not building
- Implementation detail: Paths, components, APIs, schema changes, state flow
- Acceptance criteria: Concrete checks that can become tests
Addy Osmani's six-section format is a good default. It's structured, short, and built for agents instead of committee writing. If you want a practical template, use this guide on how to write technical specifications.
PRD vs Spec A Direct Comparison for AI Teams
The difference becomes obvious at this point.
| Axis | PRD (Product Requirements Doc) | Spec (Technical Spec) |
|---|---|---|
| Primary job | Align humans on problem and outcome | Instruct implementation |
| Main audience | Co-founder, designer, marketer, future team | AI coding agent, engineer, reviewer |
| Level of detail | High-level product intent | Codebase-aware build plan |
| Best moment to use it | Before solution details are fixed | Right before and during execution |
| Success test | Shared understanding | Acceptance criteria and working code |
Hand a PRD to an AI agent and you'll usually get guesswork. Hand a spec to a non-technical stakeholder and you'll get confusion. The mismatch causes most of the friction people blame on AI tools.
Here's a useful conversation on the workflow from a PM angle:
The useful stack for small AI teams is simple. A one-page PRD if humans need alignment. A real spec if code needs to ship.
How to Avoid the Spec-Driven Development Traps
The criticism is valid when the spec is huge, stale, and detached from the code.
“Frozen specs” happen when someone writes a big document up front and never updates it. “Spec drift” happens when the code changes and the document doesn't. “Productivity trap” happens when writing the spec becomes the work.
The fix is smaller scope
Keep specs tied to one unit of work. One feature. One refactor. One bounded change. Not a master document for the entire company vision.
The r/ProductManagement thread on working with engineers in spec-driven development is useful here because it shows the human side of the failure mode. Teams struggle when specs become handoff theater instead of a live coordination tool.
A better operating rule
- Keep it narrow: One task or feature per spec
- Keep it live: Update the doc when the plan changes
- Keep it testable: Every important claim should map to a check
- Keep it disposable: When the work is done, archive it and move on
Small specs age well. Huge specs rot fast.
If you want the longer argument on where this goes wrong, read when spec-driven development becomes a productivity trap.
Choosing Your Workflow With AI Agents
If you're solo, the decision is blunt.
Use a PRD when you need clarity on the problem. Use a spec when you need correct implementation. Most of the time, shipping with AI agents means you need the second one more than the first.
That's why the solo founder gap matters. Data shows 78% of indie developers abandon projects due to unclear requirements, and the same source says a 2025 McKinsey study points to modular, AI-readable specs for solo builders rather than monolithic PRDs, as summarized in Signal Thinking's writeup on why PRDs break in AI workflows. That tracks with what happens in practice. You don't need more planning theater. You need a codebase-aware plan you can hand to Cursor, Claude Code, Codex, or Gemini.
One option built for that workflow is Tekk.coach. It connects to your GitHub repo, reads the codebase, and produces a structured spec you can manually hand to your coding agent. It doesn't create PRs, and it doesn't orchestrate external agents. It handles the planning artifact.
If you don't have a team to align, don't copy a team workflow.
The short answer to “Are specs better than PRDs?” is no. They're narrower. They're more executable. And for AI-driven development, they're usually the document that gets the work shipped.
Connect your GitHub repo. Describe the problem. Get a structured spec. Ship with Tekk.coach.
Part of the Spec-Driven Development pillar — a 52-page honest playbook on shipping with AI coding agents.