Thin-spec method for AI feature work that stays on track

Why AI feature work drifts

AI feature work often drifts before code starts. A team opens a chat window, writes a prompt, and judges the output. It feels fast. It also skips the part that keeps everyone aligned: naming the user problem in plain words.

When the prompt becomes the spec, the tool starts making product decisions. It guesses the flow, tone, edge cases, and even what "done" means. Some guesses look fine in an early review, so the drift stays hidden until design or code is already underway.

Long documents do not always fix this. A ten-page spec can still dodge basic choices: who the feature is for, what pain it removes, and what should happen when the input is messy or incomplete. Those gaps surface late, when nobody wants to reopen the plan.

Vague goals make it worse. "Make support replies better" or "improve onboarding" can lead to very different drafts. One tool writes copy. Another builds routing logic. A third adds analytics and settings nobody asked for. All of it can look polished. None of it may solve the real problem.

That is why teams end up arguing after the expensive work starts. They did not agree on the problem first, so they argue about the draft, the UI, or the code that already exists. By then, every opinion feels personal.

The thin-spec method cuts off drift early. Write a short problem statement first. Add hard acceptance cases next. Let the tool fill in the routine detail only after the boundaries are clear. That simple order saves a lot of rework.

What a thin spec needs

A thin spec is short, but it is not vague. It gives the tool enough direction to stay honest and nothing more.

Start with one problem statement. Keep it to a few lines. Say who has the problem, what goes wrong now, and what should happen instead. If it takes three paragraphs to explain, the task is still fuzzy.

Four parts are usually enough:

• who feels the pain
• what fails or takes too long
• what result counts as good enough for this release
• where the feature applies, and where it does not

Then add a small set of hard acceptance cases. These are plain pass or fail checks. A teammate should be able to test them in a few minutes.

For example, instead of saying "make inbox triage better," say what must happen: urgent billing emails go to the billing queue, duplicate messages do not create a second ticket, and unclear messages stay unclassified instead of forcing a guess. That last rule matters. Good specs tell the system when to stop.

Non-goals belong here too. Name what the feature will not do, what data it cannot use, and which edge cases you will leave for later. That keeps scope from quietly expanding halfway through the work.

The point is simple. People set the boundaries. Tools fill in the blanks.

Write the problem in a few lines

Most bad specs start too wide. They open with strategy, bug history, or a design debate. That gives the tool too much room to guess.

Start with one person, one moment, and one blocked action. If the reader cannot picture the scene, the spec is still too loose.

A short problem statement should answer four questions: Who is stuck? When does it happen? What stops them now? What result is enough for this release?

For example: "A support agent reviewing a new ticket cannot tell whether the customer reported the same issue in the last 30 days. The agent opens several past tickets and reads notes by hand. Good enough for this release: when the ticket opens, the system shows up to three likely related tickets with a short reason for each match. If no match is strong, show nothing."

That works because it names the user and the exact moment. It also describes the block in plain words. "Opens several past tickets and reads notes by hand" is much clearer than "the workflow is inefficient."

Leave out anything that does not change the job to be done. Old meeting notes, strong opinions, and early design choices only blur the problem. "We have wanted this for a while" adds nothing. "Use embeddings and a side panel" is too early unless the method is fixed for a real reason.

Before you move on, read the paragraph once and ask a blunt question: could two people picture the same user moment from these lines alone? If not, trim it again.

Write a thin spec in one pass

Draft the first version in one sitting. If you polish every sentence as you go, the spec grows while the idea gets less clear.

Start with the problem, not the interface. Write three to five sentences about who has the problem, what goes wrong today, and what should change. Keep the language plain.

Then write five to eight acceptance cases. Cover normal use, messy input, and the one or two things that must never happen. Use concrete wording such as "returns one clear label" or "does not send a reply without human approval."

Add two or three non-goals before you ask any tool to help. This step saves time because it blocks the usual extras: analytics, admin settings, or a full rewrite of an old screen.

A simple pass looks like this:

1. Write the problem in a few sentences.
2. Add plain acceptance cases.
3. List the non-goals.
4. Ask the tool to draft copy, tests, and edge-case notes.
5. Check the draft against the acceptance cases before anyone starts building.

That last review matters more than most teams think. Tools often produce neat drafts that feel complete, but they still skip awkward cases. A spec can sound polished and still miss the one condition that breaks the feature on day one.

Set acceptance cases tools cannot dodge

Acceptance cases keep AI feature work honest. A weak spec can still produce a polished draft, so the checks need to be clear enough that a person can verify them with their own eyes.

Write outcomes, not intentions. "The app groups duplicate support tickets and shows one suggested reply" is testable. "The app handles tickets well" is not.

Cover the normal path first. Then add the awkward cases people forget. AI features often look fine on clean input and fail on missing fields, timeouts, or uncertain answers. A short spec should still say what happens when the model times out, when the text is too long, or when confidence is low.

Real numbers help because they force decisions. For example:

• accept a CSV with up to 5,000 rows
• return the first result in under 15 seconds
• if confidence drops below 0.75, mark the row for review
• if the input is empty, show an error and do not call the model
• keep the original user text visible in the review screen

Words like "fast" and "easy" cause trouble because everyone reads them differently. Replace them with a time limit, a threshold, or a visible behavior. "Fast" becomes "shows a draft reply in under 8 seconds." "Easy" becomes "a support agent can approve or reject the draft in one click."

One hard case usually beats a long paragraph. A few specific checks leave far less room for drift than a page full of broad goals.

Let tools draft the rest

Once the spec has a clear problem statement and hard acceptance cases, let the tool handle routine detail. Do not let it rewrite the rules.

A good prompt asks the tool to turn each acceptance case into buildable material: one test per case, empty states for when no data exists yet, error handling for broken inputs, and notes on what to log when something fails. Those drafts are useful because they save effort on the repetitive parts.

Keep generated detail separate from the source spec. Put the thin spec in one file. Save AI output in a draft file for tests, edge cases, and implementation notes. That makes review faster and safer. If the tool invents business rules, you can delete the draft and run it again without touching the real spec.

This failure pattern shows up all the time: the tool reads one example and turns it into a rule. If your spec says, "Sort open tickets by oldest first," and the draft adds "high priority tickets always jump to the top," the model just made a product decision for you. Do not edit around it. Tighten the prompt and say, "Do not add ranking logic beyond the acceptance cases. If a rule is missing, mark it as open."

Teams that use AI well treat prompts as working instructions, not magic. When a draft invents rules, narrow the prompt, regenerate, and move on.

A simple example from a support inbox

A small support team does not need a long spec to build something useful. Imagine one agent pastes a customer email thread into an internal tool. The tool reads the thread, suggests a reply, and adds an urgency tag so the team knows what to answer first.

That job is narrow, which makes it a good fit for the thin-spec method. You are not asking the model to redesign support. You are asking it to do two things: draft a reply and sort the message by urgency.

A short problem statement could be:

"When a support agent pastes a customer thread, the system drafts a reply in the company tone and tags the thread as low, normal, high, or manual review. The draft must use facts from the thread only. If details are missing, it must ask for them."

The acceptance cases keep the build on track:

• If the customer says, "My order still has not arrived," but gives no order number, the draft asks for the order number. It does not invent one or promise a shipping status it cannot verify.
• If the thread includes threats, abusive language, or signs that someone may be unsafe, the system marks it for manual review or high urgency. The reply stays calm and short.
• If the thread includes an order number and a simple delivery question, the system can draft a normal reply and tag urgency based on the issue.
• If the model is unsure, it says less and sends the case to a person.

That last rule matters. A weak draft costs a few seconds to fix. A confident wrong answer can lead to refunds, chargebacks, or an angry customer.

Mistakes that waste time

Most delays do not come from the model itself. They come from fuzzy instructions around it.

One common mistake is stuffing the spec with edge cases on day one. Teams try to predict every strange input before they have seen real usage. That feels careful, but it usually slows the first build and hides the main job. Start with the normal path and a few hard failure cases. Add more only after you see where the tool slips.

Another mistake is writing acceptance cases that describe code instead of results. "Use a vector store," "call this function," or "rank with this prompt" are build choices, not pass or fail rules. A good case says what the user should get. "The draft reply keeps the refund amount correct" is useful. "Use chain of thought and a scoring step" is not.

Teams also lose time when they change rules inside prompts and nowhere else. That creates a hidden spec. One person updates the system prompt. Another reads the old document. A week later nobody knows whether the tool failed or the rules changed. If a rule matters, put it in the shared spec and update the acceptance cases.

Ownership matters too. If nobody owns the final spec, it turns into a pile of comments, prompt edits, and chat messages. Engineers build from one version. Product reviews another. The tool gets blamed for confusion people created.

The warning signs are easy to spot:

• the spec grows faster than the feature
• reviewers argue about prompts instead of outcomes
• tests pass, but users still get bad results
• nobody can say which document is current

Quick checks before you start building

A thin spec should survive a cold read. If a new teammate cannot explain the problem in 30 seconds, the spec is still foggy.

Before anyone starts building, read the problem statement out loud. Then read each acceptance case and ask one blunt question: would two different people score this the same way? If not, rewrite it.

A quick review takes about five minutes:

• ask someone outside the planning chat to explain the problem back in plain words
• check every acceptance case for a clear pass or fail result
• add at least one failure path for missing data, bad input, or unclear intent
• put non-goals in their own short block so nobody mistakes them for unfinished work

This catches drift while it is still cheap to fix. "Suggest replies for customer emails" sounds fine, but it leaves too much room. Add cases like "do not draft a refund promise without order data" and "show 'need human review' when the message intent is unclear," and the team has something solid to build against.

What to do next with your team

Pick one small feature this week and test the thin-spec method on it. Do not start with a big migration or a messy core flow. Choose something contained, like a support inbox label rule, a reply suggestion box, or a small admin action that already causes back and forth.

Keep the routine simple. One person writes the short problem statement. Another writes hard acceptance cases. Then let your AI tools draft tickets, edge cases, or implementation notes from that input. If the draft wanders, the spec is still too fuzzy.

After the feature ships, compare it with your last AI project. Look at rework, not opinions. Count how many times the team had to rewrite prompts, reopen tickets, change behavior after review, or explain the same rule twice. Even a rough comparison is enough. If thin specs cut one or two rounds of confusion, the method already paid for itself.

Save a shared template right away. Keep it plain: problem statement, constraints, acceptance cases, and a short note on what the team will not handle yet. That last part stops extra scope from sneaking into the draft.

If your team keeps getting uneven results, an outside review can help. Oleg Sotnikov at oleg.is works as a Fractional CTO and startup advisor, and this kind of process work fits naturally into that role. A fresh review of how your team writes specs, uses AI tools, and moves from draft to delivery can reveal gaps that feel normal from the inside.

Start small. Measure rework. Keep the template. Then run the same test again next week.