There’s a category of bug that doesn’t show up in your test suite, doesn’t trigger a Sentry alert, and doesn’t get caught in code review. It lives in your documentation.
It looks like this: a developer copies your “Getting Started” code sample, runs it, and gets an error. The parameter name changed three releases ago. The response shape is different now. The authentication header format was updated. Nobody thought to update the docs.
The developer doesn’t know if they’re doing something wrong or if the docs are wrong. They spend an hour troubleshooting. They open a support ticket. They post in your community Slack. Some of them quietly decide your product is too painful to integrate with and move on.
This is the most preventable category of developer experience failure. And the fix is straightforward: test your code samples.
Why most docs teams don’t do this
Testing code samples isn’t hard. It’s just easy to skip.
The typical documentation workflow looks like this: an engineer explains a feature, a writer translates that explanation into a guide, the guide gets reviewed for accuracy by the engineer who explained it, and then it ships.
The problem is that “reviewed for accuracy” usually means “read by someone who already knows how it works.” It doesn’t mean “run by someone who’s trying to use it for the first time.” Those are very different checks.
And then, six months later, someone renames a field, updates the auth flow, or changes the error format — and the docs don’t get updated because nobody is watching them for correctness. The engineer who made the change didn’t think to check the docs. The docs author wasn’t watching the diff.
The code samples go stale. Silently.
My process
For every guide or tutorial I write, I run through a specific sequence before anything gets published.
Step 1: Build the thing first, then write about it.
I don’t write documentation from a spec or from memory. I build a working integration — actual code, running against the actual API — and then I document what I did.
This sounds like double the work. It isn’t, for two reasons:
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Building the integration first reveals every gap in the spec before it becomes a gap in the docs. I find the missing parameters, the undocumented errors, the behavioural quirks that aren’t reflected anywhere in the API contract.
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The code I write while building the integration often becomes the code samples in the docs, with light editing. Starting from working code and extracting documentation is significantly faster than writing documentation and then verifying it against code.
Step 2: Use a fresh environment for every walkthrough.
If I’m documenting how to authenticate and make a first API call, I do that walkthrough from a clean state — no cached credentials, no pre-existing objects, no environment variables that have been there for months.
The developer following the guide is starting fresh. The only way to know if the guide works for them is to follow it in conditions that match theirs.
This catches things like: “I have to create a resource X before I can create resource Y, but the guide starts at Y” — which is invisible if you’re testing in an environment where X already exists.
Step 3: Copy the code samples from the docs, not from my editor.
By the time I’m reviewing a finished guide, I have clean working code in my editor. It’s tempting to run that to verify the docs. Don’t.
Instead, I copy the code sample exactly as it appears in the documentation — from the rendered page, where formatting can introduce issues — and run that. The number of times I’ve found a subtle bug this way (a curly quote that crept in, an indentation issue that broke a YAML block, a line break that split a string) is embarrassing.
Step 4: Check error responses too.
Most documentation testing focuses on the happy path. Make the request, get the 200, verify the response looks right.
I also verify the error cases. I make requests with missing required fields, with invalid values, with expired credentials. I check that the error codes and messages in the docs match what the API actually returns.
This matters because error documentation is often the most out-of-date part of any API reference. Error formats change, error codes get added, error messages get reworded — and the docs rarely catch up.
What to do when something breaks
When I run a code sample and it fails, I follow a simple decision tree:
- Is the docs example wrong? Fix the example.
- Is the API behaving unexpectedly? File a bug with the engineering team. Don’t ship the docs until it’s resolved.
- Is there a gap between the spec and the actual behaviour? Update the spec, then update the docs.
The important thing is that a broken code sample never gets published. If I can’t make a specific example work in a clean environment, the guide doesn’t ship until I can.
The compound return
The payoff from testing code samples isn’t just “the docs are accurate.” There are second-order benefits that compound over time.
You become the team’s first real test of developer experience. Before any external developer touches the API, you’ve already found the friction points — the confusing parameter names, the missing required context, the errors that are technically correct but not actionable.
Engineers start trusting the docs in a different way. When a code sample lives in the documentation, they know it was tested. That means they can reference it with confidence when answering developer questions.
And the support burden drops. Not entirely — documentation will never replace good support — but when the getting-started guide actually works, fewer people need help getting started.
The discipline of testing code samples is one of the most impactful things a Docs Engineer can do. It’s not glamorous. It adds time to every release cycle. And it is, in my experience, the single most effective way to build developer trust in a product.
If your developers are struggling to get started, the first thing I’d check is whether anyone has run your getting-started guide from a fresh environment recently.
The answer is often: no. And the fix is often: do that.