Although I don’t work in road ecology or traffic engineering, the author somehow pulled me through 300 pages on this topic. He managed this not just through vivid language and diction, but by personally visiting places and telling stories about the specific challenges that animals, “carers,” forest service workers, and others faced as freeways and highways bisected and dissected their environments.
To use an analogy, suppose you’re a barista making espresso coffee. An AGI-capable robot trained as a barista is able to make all the coffee that a regular barista can make but twice as fast. Further, the Android barista can create exquisite espresso art in any shape that humans request, wowing them and making the experience novel. Soon the human barista is replaced. After all, the paying customer would rather pay $2.50 for a robot to make a latte instead of $5.00, especially when it tastes the same.
Most code samples in documentation are fairly basic, which is by design. Documentation tries to show the most common use of the API, not advanced scenarios for an enterprise-grade app whose complexity would easily overwhelm developers. (At that point, you end up with a sample app.)
With AI tools built directly into your authoring tool or IDE (such as VS Code), fixing simple doc bugs can become a mechanical, click-button task. Here’s the approach to fixing simple doc bugs:
(Note: The fact that I’m writing a book review on this topic might seem odd given that I usually focus on tech comm topics. However, I document APIs for getting map data into cars, so I sometimes read books related to the automotive and transportation domain. I also run a book club at work focused on these books.)
During the past few weeks, I’ve felt like my brain’s RPMs have been in the red zone. Granted, the constant stream of chaotic political news hasn’t helped—but regardless of current political events, I’m frequently checking the news, my email, and chat messages and operating in a mode that isn’t great. Reading long-form books has proven to be difficult. I run a book club at work focused on automotive and transportation books, and it took me two months to make it through a single book (granted, it was a 300-page historically dense book, but still).
“Biohacking” might be a pretentious cyber term for what is otherwise a straightforward experiment. For 10 days, I tracked my food and exercise levels while also wearing a continuous glucose monitor (CGM) to track my glucose levels. I then used AI to pair up the food + exercise with the glucose readings and perform an analysis about triggers for glucose spikes and recommendations to avoid them.
I want you to act as my AI stream journal (similar to a bullet journal), for the day. In this chat session, I’ll log 3 kinds of notes: tasks, thoughts, and events. Tasks are to-do list items. Thoughts are random ideas or notes I have. Events consist of food eaten, exercise, or descriptions of my internal states. The point is to have an easy way to dump all the scattered information in my head into a central log that you organize and analyze on my behalf.
After completing the API reference tutorial, you’re ready to start an activity that gives you more experience in creating and editing API reference documentation.
In this activity, you’ll evaluate some API reference topics to identify issues.
The sections might be named differently in the API doc sites you browse, but they’re usually recognizable to some degree (if included). If you’re finding it somewhat difficult to locate them, this is part of the wild west of terminology and organization when it comes to API documentation.
Assess the API reference documentation by answering the following questions for each section:
Resource description:
Is the description action-oriented?
Is it a brief 1-3 sentence summary?
Endpoints and methods:
How are the endpoints grouped? (Are they listed all on the same page, or on different pages? Are they grouped by method, or by resource?)
How are the methods specified for each endpoint?
Parameters:
How many types of parameters are there (header, path, query string) or request body for the endpoints?
Are the data types (string, boolean, etc.) defined for each parameter? Are required/optional values noted?
Request example:
In what format or language is the request shown (e.g. curl, specific languages, other)?
How many parameters does the sample request include?
Response example:
Is there both a sample response and a response schema? (And is each element in the response actually described?)
How does the doc site handle nested hierarchies in the response definitions?
Now that you understand the essential sections to cover in documenting API endpoints, let’s look at standardized approaches for describing these sections, primarily with the Overview of REST API specification formats.
The OpenAPI standard will help make sure you cover all the necessary details in these sections, and it will present the information to users in a way that users have become accustomed to.
About Tom Johnson
I'm an API technical writer based in the Seattle area. On this blog, I write about topics related to technical writing and communication — such as software documentation, API documentation, AI, information architecture, content strategy, writing processes, plain language, tech comm careers, and more. Check out my API documentation course if you're looking for more info about documenting APIs. Or see my posts on AI and AI course section for more on the latest in AI and tech comm.
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