Design docs for generated code with clear intent notes

Why this code feels hard to review

Reviewers usually see the output, not the choice behind it. The code runs, the tests pass, and the diff looks busy, but none of that explains why the change exists or which parts must stay fixed.

The gap gets wider with AI-written code. A small request can turn into edits across five files, a new helper, a prompt template, and glue code nobody planned to touch. The reviewer has more text to read even when the real change is only one rule.

That is why generated diffs often feel heavier than hand-written ones. A developer asks for "add summaries to support tickets" and gets retry logic, formatting helpers, error handling, and config flags along with it. Some of that is useful. Some of it is noise. The hard part is telling the difference.

Hidden rules make review even slower. The behavior people care about often sits inside a helper name, a prompt sentence, or a default value buried in a wrapper. If a reviewer misses one of those details, they can approve a product change by accident.

So reviewers start reading backward. They scan the diff, jump into helpers, search for old behavior, and try to guess what the author meant to protect. That takes time. It also leads to weak comments because people argue about code shape instead of intent.

On teams that use AI generation often, this happens constantly. The reviewer is not just checking correctness. They are rebuilding the design from fragments.

A simple example shows the problem. If a tool adds AI summaries to support tickets, one reviewer may think the summary only needs to be readable. Another may assume it must never expose private customer data. The code alone may not make that rule obvious, especially if the safety check lives in one helper and the prompt sits in another file.

Design docs fix this by putting the missing context next to the change. Without that context, people keep rereading code just to guess what the author wanted to keep stable.

What your design doc should answer

Generated code often looks finished before anyone understands it. A design doc needs to explain intent first. Reviewers should learn what problem the change solves, which rules stay fixed, and where the work ends before they read a long diff.

Start with the problem in plain language. Skip tool names and internal jargon for the first few lines. Write the user or business issue the way you would explain it to a new teammate: support agents lose time writing the same summary, or a billing job fails when input arrives out of order. If the problem statement is fuzzy, the review will be fuzzy too.

Then mark the parts that must not change. These are the guardrails. Maybe the API response shape stays the same, old reports must still match past totals, or a human must approve any customer-facing text. Reviewers need this early because it tells them whether the generated code drifted into places it should not touch.

Explain why you picked this approach and not another one. Keep it short. One or two concrete reasons are enough: this option reuses the current queue, avoids a schema change, or keeps failure handling in one service. You do not need an essay. You need enough detail for a reviewer to compare the code with the plan.

Scope also needs a clear edge. Say where the change stops. If the new logic only covers ticket summaries in English and does not change routing, say that directly. Boundary notes save time because reviewers stop hunting for work that was never part of the change.

It also helps to name the rules reviewers should protect. For example, inputs from other systems still need validation, logs must not store secrets or customer message content, existing rate limits still apply to new background jobs, and fallback behavior should stay simple when the model fails.

If you can answer those points in one page, review usually gets faster and calmer. People spend less time on style arguments and more time on decisions that affect the product.

Use decision records for the big choices

Generated code often hides the moment when a real choice was made. Reviewers can see a table, a queue, or a retry loop, but they cannot see why that shape exists. A short decision record gives them the reason before they read 800 lines of code.

Write one record for each choice that changes behavior, cost, or future flexibility. Good candidates include storage, auth, background jobs, caching, model selection, and third-party services. If a bad choice would hurt to undo next month, it deserves a note.

A useful record can stay small:

• A short name and date, such as "Use PostgreSQL for ticket summaries - 2026-04-13"
• The option you almost picked instead
• Why you chose this path now
• What would make you change it later

That third point should be specific. Do not write "chosen for scalability" and move on. Write what you actually mean: "We kept summaries in PostgreSQL because the team already backs it up, query volume is low, and adding Redis would add setup and monitoring work for little gain."

The second point matters just as much. When you name the option you nearly picked, you save the next reviewer from reopening the same debate from scratch. They can judge whether your tradeoff still makes sense instead of guessing whether you ignored another option.

The last point keeps the record honest. A decision record is not a forever promise. It is a note about what makes sense under current conditions. Add a trigger such as "switch to a queue if summary jobs start delaying ticket updates" or "revisit model choice if cost per 1,000 summaries doubles."

That is enough for most generated-code changes. Six weeks later, a reviewer can compare the code to the stated choice, check the trigger, and decide quickly whether the implementation still fits the intent.

Add boundary notes at the edges

Generated code usually breaks trust at the edges, not in the middle. A reviewer can skim a neat function and still miss the real risk: what the code reads, what it sends out, and what it assumes is safe.

That is why design docs need a short boundary note. It tells the next reviewer where the code starts, where it stops, and which assumptions deserve the most scrutiny.

Write down which inputs the code trusts without extra checks and which ones it does not. Be direct. If the job accepts ticket text from your support system, say that. If it ignores attachments, pasted HTML, or free-form prompts from users, say that too.

A good boundary note names every outside dependency the code touches, such as APIs, queues, files, webhooks, or model providers. It sets hard limits for size, run time, and spend. It states what the code will never do, even if the input asks for it. It also marks data that needs extra care, like email addresses, payment details, internal notes, or health data, and it says where failures stop: whether one bad record skips, retries, or halts the whole run.

Numbers help more than warnings. "Reads up to 100 tickets, stops after 20 seconds, and caps model cost at $1 per batch" gives a reviewer something they can test. "Uses the OpenAI API and one prompt file from the repo" is much better than making them hunt through generated code.

Refusals save time too. If the code only creates summaries, say that it will not reply to customers, change ticket status, delete records, or write back to the CRM. Reviewers stop chasing side effects that should never exist.

Sensitive data deserves its own line. If the code redacts names before sending text to a model, say so. If it skips tickets with billing data or private notes, say that too. People can judge risk much faster when the boundary is written in one place.

A short boundary note often saves more review time than another page of diagrams. Reviewers usually need intent first, and boundary notes give them that quickly.

Write the doc before you ask for review

Ask for review only after you can explain the change in plain language. If the first thing a reviewer sees is a large generated diff, they will spend most of their time guessing why the code exists at all.

A short doc fixes that. It does not need to describe every helper, wrapper, or copied pattern. It only needs to give reviewers enough context to decide whether the change makes sense before they read the noisy parts.

Start with one sentence about the user or business need. Keep it concrete. "Support agents need ticket summaries so they can reply in under two minutes" works much better than "improve workflow."

Then record the choices that will outlast this pull request. If you picked one model, one storage shape, one retry rule, or one place to run the job, give each lasting choice a short decision record. A reviewer should see what you chose, why you chose it, and what you ruled out.

Boundary notes matter just as much. Generated code often looks fine in the middle and gets risky at the edges. Put notes where the code touches customer data, permissions, rate limits, external APIs, and failure paths. If a summary job can expose private text, retry twice, or fail closed, say that directly.

You do not need to map the whole repository. Name only the files that matter for review. In most cases, that means the entry point, the module with the main decision, the schema or config change, and the place where errors surface to users.

A review doc often fits on half a page: one sentence on the need, a few decision records for choices that will stick, boundary notes for data, access, and failures, and a short file map for the reviewer.

Stop when a new reviewer can judge intent without reading generated code line by line. If they still need to open twenty files to understand the change, the doc is not done yet. If they can say "yes, this solves the right problem" or "no, this creates too much risk" in a few minutes, you wrote enough.

A simple example: adding AI summaries to support tickets

A support team wants a short AI summary on every new ticket so agents can scan the issue before they open the full thread. It sounds small, but the code can spread quickly across message intake, prompt building, redaction, model calls, retries, storage, and the agent dashboard. If some of that code was generated, reviewers can get stuck reading helper after helper without learning why the feature works the way it does.

A short design doc clears that up. The decision record explains the feature rules, and the boundary notes show where the system must stop.

In this case, the decision record says the summary stays internal. Agents use it to save time, but customers never see model-written text by default. The team wants a human to approve any reply that leaves the company because a summary is useful for triage and still risky as customer-facing language.

The boundary notes would say this plainly:

• The model gets only the ticket body, subject, and a small amount of account context.
• The request has a hard token cap so one huge ticket does not blow up cost.
• The redaction step strips account secrets, reset links, API keys, and similar sensitive text.
• The system stores the summary as an internal note, not as part of the customer thread.
• An agent must approve any response that uses the summary.

That gives reviewers something concrete to judge. They do not need to read every helper to know whether the design makes sense. They can ask direct questions instead: does the code enforce the token cap, does it block secrets before the model call, and does it keep human approval in the flow?

That is why design docs matter here. The code may be noisy, but the intent should stay easy to inspect. When the doc names the choices and the limits, AI code review gets much faster and much less frustrating.

Mistakes that waste review time

Review slows down when the doc reads like a tour of files instead of a note about intent. If you describe what the code does only after the work is done, reviewers still have to guess why you chose that shape. They end up reading generated code line by line because the goal is missing.

A short design note should name the choice first. "We cache summaries for 24 hours to keep support pages fast" is useful. "Added cache layer and updated handlers" is not. The second sentence tells people what changed, but not what you were trying to protect.

Another common mistake is hiding the road not taken. If you skip the rejected option, the tradeoff disappears, and the review turns into a repeat of old debates. One line can save ten comments: "We did not summarize on every page load because API cost grew 4x in testing." That gives the reviewer something solid to judge.

Teams also bury limits in code comments where almost nobody will see them during review. Boundary notes belong in the doc, next to the decision itself. If the feature fails for tickets over 10,000 characters, say that up front. If the job runs only in one region, say that too. Reviewers should not have to discover limits by opening helper files and tracing conditionals.

Long essays cause a different problem. Nobody updates them. After two or three changes, the document turns into stale fiction, and reviewers stop trusting it. A one-page note with a few decision records usually ages much better than five pages of background.

Vague words waste time fast. "Better," "cleaner," and "simpler" mean almost nothing on their own. Give a reason that someone else can test. Say the new flow cuts retries from three calls to one, removes a flaky dependency, or makes rollback possible in under five minutes.

A blunt test works well: can a reviewer explain the intent, limits, and tradeoff without opening every file? If not, the doc is still too fuzzy.

A short checklist before you merge

If a reviewer cannot explain the change after two minutes with the doc, the doc is still too loose. Generated code often looks busy, so review should start with intent, limits, and scope, not line-by-line guessing.

Use this check before you ask anyone to approve the merge:

• Write one sentence that names the choice you made and why.
• Mark the rules that must not drift.
• Make every boundary note concrete with real limits.
• Show where the change stops.
• Read the doc as if the code will move six months from now.

That last check matters more than people think. File paths change. Function names change. A good note still makes sense after a refactor because it explains behavior and decisions, not placement.

One simple test works well. Give the doc to someone who did not build the feature and ask three questions: what choice did we make, what rule must stay fixed, and where does this change end? If they hesitate, the merge can wait.

That may feel strict, but it saves time later. Clean code can still confuse people if the design note is vague. A short, sharp doc gives reviewers something firmer than a noisy diff.

Next steps for your team

Teams get better results when they stop inventing a new review method for every AI-assisted change. Pick one small template and make it the default. A useful template only needs five parts: change summary, intent, decision record, boundary notes, and rollback plan.

When everyone uses the same shape, reviewers spend less time guessing. They can focus on whether the change makes sense, not on decoding how the author chose to explain it.

Ask reviewers to read intent first. Many teams still open the diff and scan file by file. Generated code makes that slow and frustrating. A one-minute read of the doc can save twenty minutes of diff hunting because the reviewer already knows what the code should do, what it should never do, and which tradeoffs the author accepted.

Keep decision records close to the code, not in a wiki people forget to open. If the team later swaps a queue, changes model limits, or removes a cache, update the note in the same pull request. Old decisions age fast when the code keeps moving.

A simple team routine is enough:

• Use one template for every AI-assisted change, even small ones.
• Put the design note in the repo next to the code it explains.
• Ask reviewers to approve the intent before they inspect the diff.
• Update the decision record when a choice changes during review.

If you want a lighter process, make one rule non-negotiable: every merge needs a short intent note. Everything else can stay flexible. That usually works better than a thick review policy nobody reads.

If your team needs help setting up a practical process around AI-generated code, Oleg Sotnikov at oleg.is works as a Fractional CTO and startup advisor on AI-first development and automation. For small teams, an outside review structure like that can be enough to turn design docs into a normal habit instead of extra paperwork.