How to Write an AI Product Spec Engineers Can Actually Ship
The spec template we use at Augere Labs to turn a founder's Loom into a scoped, buildable AI product in one week.
Most AI projects that miss deadlines don't miss because the engineering was hard. They miss because the spec was ambiguous — everyone in the Slack channel had a slightly different picture of what "the AI feature" was.
At Augere Labs we've shipped enough AI products to notice a pattern: the ones that come in on time have a 4–6 page spec written before a single line of code. The ones that slip have a Loom and a Notion doc titled "Ideas."
This is the template we actually use with founders. It's boring on purpose.
What an AI spec has to answer
A good AI product spec answers five questions in order. Skip any of them and you'll pay for it in scope creep, model bills, or a shipped feature nobody uses.
- What is the user trying to do? (Job to be done)
- What does success look like for that user and the business?
- What are the inputs and outputs of the AI part?
- What is not in scope? (The most-skipped section, and the most important.)
- What happens when the AI is wrong? (The section that separates senior teams from vibe-coders.)
The one-page front matter
Every spec we write opens with a single page containing four fields. If you can't fill this in, you're not ready to write the rest.
- Feature name: a short, boring name. "Reply Suggestions," not "AI Copilot 2.0."
- Primary user: one persona. Not three.
- The one sentence: "When [trigger], [user] wants [outcome] so they can [benefit]."
- Success metric: one number, one deadline. "40% of drafts sent within 30s of AI suggestion, measured by day 60."
If your success metric is "users love it," rewrite it. Love is not a measurement.
The core loop
The core loop is the sequence of user actions and system responses that make up the feature. For AI products, we always draw it as a state diagram before writing prose.
A common pattern we see: founders describe the AI in terms of the model ("it uses GPT-4o and Claude with a router") when they should describe it in terms of the user's turn: what they type, what they see, what they can undo.
Example core loop for an AI meeting-notes tool:
- User joins a call → recorder starts automatically.
- Call ends → transcript appears within 60 seconds.
- User opens the note → summary, action items, and unanswered questions are already there.
- User edits any section → edits are saved as their preferences (used to improve future summaries for them).
- User shares → link with configurable redaction.
Notice what's missing: nothing about models, prompts, or vector stores. Those are engineering decisions the spec informs, not the other way around.
The AI part — what to actually write
This is where most PRDs get vague. Founders write "AI will summarize the transcript." That's not a spec, that's a wish. Here's the shape we use.
Inputs
List every input the model sees, including the ones from the system: user text, uploaded files, prior conversation, tenant metadata, feature flags, and any tool results. This is where privacy and PII questions surface early — right where they're cheap to fix.
Outputs
Define the exact shape. If it's structured, paste the JSON schema. If it's free text, define the length, tone, and forbidden content. "A 3–5 bullet summary, under 60 words total, no personal opinions" is a spec. "A summary" is not.
Quality bar
Every AI feature needs an eval set — 20 to 50 real examples with the answer you'd accept. Add them as an appendix. If you can't produce 20, you don't understand the feature well enough to build it.
Latency and cost budgets
How long can the user wait? What's the max spend per user per month? These decide model choice more than any benchmark does. Our default budgets for a B2B SaaS AI feature: p95 under 6 seconds for interactive, under $0.10 per user per month for background jobs.
Non-goals — the most valuable section
A mistake teams often make: writing what the feature does without writing what it doesn't. Non-goals stop scope creep before the standup.
Real non-goals from a recent spec we shipped:
- Will not support languages other than English (Spanish is a v2).
- Will not learn across tenants — one user's edits do not influence another user's outputs.
- Will not replace the existing rules-based tagger; it augments it.
- Will not run without an internet connection.
Every non-goal is a saved week. Read that sentence twice.
Failure modes
An AI feature without a failure-mode section is a bug factory. This is where we make explicit what happens when the model is wrong, slow, or refuses.
Every AI feature we ship has four defined states:
- Confident and correct — the happy path.
- Confident and wrong — how does the user notice and correct it?
- Uncertain — does the UI show that? What does "I don't know" look like?
- Failed — timeout, rate limit, or refusal. What does the user see?
Design the "wrong" state before the "right" one. It'll happen more often in the first month than you expect.
Evals belong in the spec
An eval is a test case with an expected output. For AI products, evals replace unit tests as the source of truth. The spec should include:
- The seed eval set (20–50 examples).
- The scoring method (exact match, LLM-as-judge, rubric, or human review).
- The pass threshold. "The feature ships when 85% of the eval set passes at temperature 0."
Evals are the difference between "the demo worked" and "it works in production." Founders who ship real AI products treat evals the way seniors treat CI — non-negotiable.
What we intentionally leave out
A good spec is short. Things that don't belong:
- Model names. Those change. Write the requirement, not the vendor.
- Prompt text. Prompts are code. They live in the repo.
- UI mockups embedded in the spec doc. Link Figma, don't paste.
- Roadmap. This spec is v1 of one feature. Everything else is a different doc.
If your spec is 20 pages, it's not a spec — it's a graveyard.
The two people who need to sign off
Before we start building, exactly two people sign the spec: the founder (or PM) and the tech lead. Not a committee. If more than two people need to agree on a v1 spec, the org is too heavy to move fast anyway.
What happens after sign-off
The spec becomes the acceptance criteria for the pull request. Every PR references the section it satisfies. Every question that comes up in Slack ("should the summary include emojis?") gets a one-line addition to the spec, dated. The doc is a living record of decisions, not a snapshot from week one.
A common mistake we see
Founders write the spec themselves, hand it to a contractor, and disappear. Three weeks in, the contractor has interpreted 40% of the ambiguities in a way the founder wouldn't have picked. This is where the "AI project that ran over budget" story starts.
A better pattern: write the spec with the engineering lead in a 90-minute session. They'll surface the ambiguities you can't see. Two hours of pairing saves two weeks of rework.
Template you can steal
Here's the actual outline we hand founders on day one. Fill this in and you have a shippable spec:
1. Front matter (feature name, primary user, one sentence, success metric)
2. Core loop (numbered user + system steps)
3. AI part
3.1 Inputs
3.2 Outputs (with schema)
3.3 Quality bar (eval set link)
3.4 Latency + cost budgets
4. Non-goals
5. Failure modes (confident-wrong, uncertain, failed)
6. Evals (seed set, scoring, pass threshold)
7. Open questions (dated, owned)
Where this fits in a 30-day build
Our 30-day MVP process spends the first three days writing the spec, the next twenty-two shipping against it, and the last five polishing. Teams that skip step one lose those three days back in change requests during week three. It's the cheapest week you'll ever spend.
Frequently asked questions
How long should an AI product spec be?
Four to six pages for a single feature. Longer than that and you're documenting instead of deciding. If you can't fit it, split it into two features.
What's the difference between a PRD and an AI product spec?
A traditional PRD focuses on features and flows. An AI product spec adds inputs/outputs contracts, latency/cost budgets, failure modes, and evals — the parts that only matter when a probabilistic model is in the loop.
Do I need evals if I'm just prototyping?
Yes, but small ones. Even ten eval examples in a spreadsheet catch 80% of the regressions you'd otherwise ship. Evals scale with the product's importance.
Who owns the spec after launch?
The product owner. Engineering can propose changes, but the doc is a product artifact — it survives the launch and evolves with the feature.
Can I use an AI to write the spec?
To brainstorm inputs and non-goals, sure. As the final artifact, no. The value of the spec is the argument the humans had while writing it — that argument doesn't happen if a model does the typing.
Ready to write one?
If you have a Loom and want to turn it into a scoped, priced, shippable AI feature, that's most of what we do at Augere Labs. Book a 30-minute call and we'll draft the front page live.
FAQ
Frequently asked questions
How long should an AI product spec be?+
Four to six pages for a single feature. Anything longer usually means the feature is too big and should be split into two specs.
What's the difference between a PRD and an AI product spec?+
An AI spec adds four sections a traditional PRD doesn't have: input/output contracts, latency and cost budgets, failure modes, and an eval set with a pass threshold.
What's the most-skipped section in AI product specs?+
Non-goals. Teams write what the feature does but forget to write what it doesn't do, and that's where scope creep enters.
Do I need evals if I'm just prototyping?+
Yes — even a 10-example eval spreadsheet catches most regressions. Skip evals only for throwaway demos.
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