You keep developer portal content current by separating the pages that carry a fact from the code from the pages that explain a principle, then recomputing the former at every release instead of having them reread. A portal does not age all at once after a redesign: it ages page by page, at the speed of the fact each page cites. A concepts page lasts years; a quickstart goes stale with the first release that touches authentication.
This is about the surface aimed at integrators. For the support-side help center, see keeping the help center in sync with releases. For the chronological record of API changes, see the API changelog for integrators: one says what changed, the other is the reference people consult to learn how to do something.
One portal page, six months later
Take a fictional portal and its "Quickstart" page. The scenario is illustrative, but every step is ordinary.
- Day 0, redesign. The page is reread line by line. It walks through getting a key, making a first call and reading the response. Everything is true.
- Week 3. A release changes the response format: one field is renamed. The diff is in the pull request and the release note mentions it. The portal page does not: nobody realised it contained that field.
- Week 7. Another release shortens token lifetime. The authentication page still states the old duration; the quickstart, which links to it without repeating it, stays right by accident.
- Month 4. An integrator follows the quickstart, hits an error on the renamed field and opens a ticket. Support rereads the page, sees the mismatch, fixes the sample. Authentication stays wrong: the ticket did not mention it.
- Month 6. The next redesign is announced. Everything gets reread, three pages turn out to have been wrong for months, and the review is presented as catching up rather than as routine.
Nothing in this timeline is an individual's mistake. The facts moved in the code, the pages moved elsewhere, and nobody had the list of what linked the two.
Which pages go stale, and in what order
Not every portal page carries the same risk. The useful criterion is not age but fact density: how many precise values the page asserts, each one something the code can change without warning.
| Page | What it asserts | Staleness |
|---|---|---|
| Concepts, architecture | Principles, reasons | Slow: often outlasts several redesigns |
| Endpoint reference | Paths, parameters, return codes | Fast, but tooling exists (OpenAPI diff) |
| Authentication, limits, quotas | Durations, thresholds, token formats | Fast, and rarely tooled |
| Quickstart, code samples | A full path, fields, names | Fast, and silently wrong until the first error |
The table puts the risk where nobody looks: the reference is the only row whose content we already know how to generate. The rows below it (authentication, limits) and the quickstart are handwritten and cite values from the code.
Why rereading does not hold
The usual answer is a periodic review, or a "docs updated" box in the release checklist. Both share a flaw: they ask someone to guess which pages a release touched. The developer knows what they changed, not which pages mention it. The writer knows what the pages say, not what the release changed. Between the two, the list does not exist.
This is the mechanism described in documentation drift, with an aggravation specific to portals: the reader is not a customer who can write to support, but a developer who has already integrated. When the page lies, they find out in their own code, sometimes in production.
The way out: start from the code's facts, not from the pages
What changes the situation is reversing the direction of the work. Instead of starting from the pages and asking whether they are still right, start from what the code asserts today and look at which pages repeat it.
- Mark the fact-bearing pages. For each page, note the values it cites (a duration, a field, a format). Concept pages drop off the list.
- Establish what the code says. For each value, the source is the code or the last release's diff, not a writer's memory.
- Compare at every release. A gap between a cited value and the code's value is a defect to fix, with the page and the line.
- Recompute what can be. A page whose whole content derives from the code (the reference, a limits table) does not need rereading: regenerate it.
The control test is simple: pick a page at random, pull three values it asserts, and find them in the code in under ten minutes. If you cannot, the page is not verifiable, so it is not maintainable.
Where DeployIt fits
DeployIt reads the actual code (resynchronised on every push), the git history (commits + diffs) and the customer's data through its own MCP servers, and checks the code before asserting anything. In practice, the question "what does the code say about token lifetime?" gets a sourced answer you can compare with what the authentication page claims. The same question asked by an integration agent gets the same answer that would correct the page.
What DeployIt does not do: it does not edit your portal and publishes nothing on your behalf. It supplies the code's facts; the decision to change a page stays with your team. The MCP server is in early access / design partner program: the way in is here. For the documentation generated on the help-center side, see the help center generated from code.
"Our OpenAPI reference is generated, so that's covered"
It is a real gain and worth keeping: the endpoint reference is the best-tooled part. But it describes the shape of calls, not how they are used. The authentication guide, the quickstart and the code samples do not come out of a schema, and they are what a developer reads when starting out. The OpenAPI diff therefore leaves untouched the facts the table puts in its last two rows.
"Our integrators will tell us about errors"
Some will, and as a safety net that helps. The cost, though, falls on them: they lose an hour in a wrong sample, then open a ticket about one page, not about the three others in the same state. A report fixes the page where someone stumbled, not the ones they have not reached yet.
Mini-FAQ
Which page first? The quickstart: new users read it most, it is the richest in facts, and an error there costs the most trust.
Should the portal be versioned? If the API is versioned, yes, but the version describes what is promised. It does not check that the page is correct for that version.
How do you know a page has gone wrong without waiting for a ticket? By comparing its cited values with the code at every release, as in the list above. No other method warns you before the error.
In short
A developer portal goes stale page by page, at the pace of the facts it cites. Find the fact-bearing pages, compare them with the code at every release, regenerate whatever can be. DeployIt can supply the code's facts (MCP server in early access, through the design partner program); it is free during early access, with paid plans arriving in January 2027.
Frequently asked questions
Why does developer portal content go wrong between redesigns?
Because a redesign rebuilds the form and rereads the substance once. After that the code keeps changing with every release, and each page that cites a fact from the code (a parameter, a delay, a format) ages at its own pace.
Which portal pages go stale first?
The ones that cite precise values: quickstart, authentication, limits, code samples. Concept pages, which explain why, hold up far longer.
Is a quarterly review enough?
It catches what has already broken for an integrator. Between two reviews a page can stay wrong for weeks, and nobody knows which one.
Does an OpenAPI diff solve the problem?
It solves the endpoint reference. It says nothing about the authentication guide, the quickstart or the samples, where the facts are written by hand.
Continue reading
How do you keep an engineering team's onboarding docs current?
An onboarding guide goes stale because it describes the code as it was the day it was written. Pull the facts from the code itself, and keep by hand only what no code can say.
What should an API changelog for integrators contain?
An integrator reads an API changelog to decide one thing: do I have to touch my code? Six fields answer it, and most changelogs carry two.
Can you generate Notion documentation from your code without overwriting what your team writes?
Generating Notion documentation from code is the easy part; making it coexist with what your team writes by hand is the real problem. The four writing rules that decide whether the page survives.