"This article examines several more common problems occurring in API documentation. They are easily prevented or have simple workarounds but the writer must be aware of them in the first place. These underscore the importance of:

- Writers being familiar with programming and development concepts, so that,
- Writers can think like developers. Only by thinking like a developer can writers then hone in on client’s nuances, customize the developer experience, and optimize the time in the documentation. The goal is an ironic one. We want documentation so good and clear that clients don’t even have to read it. You’ll see what I mean in a moment.

The striking thing about these is how simple they seem. So much so, they are often overlooked. Afterall, how much can be said about an offset value of a returned list, or sorting? As it turns out, plenty. Thinking like developer is encompassing. Enough information must be presented so that clients know ahead of time what the field or endpoint does, returns, what the behavior is, and what each parameter does. You have to anticipate questions and understand how clients think about things. The last thing we writers want for clients is to make them experiment with values and guess outcomes. That annoys them and wastes their time. It means we didn’t do our job.

API documentation is not technical writing. It’s API documentation writing. The key differences are the dependence on code and a deep understanding of development practices. There are countless nuances and subtleties — and each one matters to developers. How can it not? You’re writing to developers about development. It needs to be precise and it needs to speak their language."

https://robertdelwood.medium.com/more-common-api-documentation-errors-26f1a8ceaaec

#APIs #APIDocumentation #TechnicalWriting #Programming #SoftwareDevelopment #TechnicalCommunication

"For our technical writing teams, the velocity of Google Cloud's development presents two core problems: how do we keep pace with documenting new features and capabilities, and how do we ensure the existing documentation remains accurate?

To accelerate the creation process, we have integrated Gemini directly into our writers' authoring environments. This acts as a productivity multiplier, streamlining common tasks like generating formatted tables from unstructured content, translating between markup languages, and applying complex style guides with a single click. More significantly, the adoption of AI solutions enables writers to focus their time on strategic documentation solutions and ensure high quality content.

Just as important as creation is validation. For years, automated regression testing has been a staple for catching bugs in code. We are now bringing that same discipline to documentation—a goal that was long considered a dream due to the ambiguity of natural language. For our quickstarts, we use Gemini to read the procedural steps and automatically generate web orchestration scripts (using frameworks like Playwright). These scripts then execute the steps in a real Google Cloud environment, automatically verifying that our documentation accurately reflects the product's behavior. We run over 100 of these tests daily, ensuring our quickstarts are continuously validated and that you can trust the steps you're following."

https://cloud.google.com/blog/topics/developers-practitioners/smarter-authoring-better-code-how-ai-is-reshaping-google-clouds-developer-experience

#TechnicalWriting #AI #GenerativeAI #GoogleCloud #SoftwareDocumentation #APIDocumentation

"Finding the right API is rarely straightforward. But once the AI locates an API, it needs to be evaluated. This is where API documentation comes in. Detailed descriptions tell AI what the API does, what data formats it uses, what authentication systems are in place, and any limitations it might have.

Good API documentation allows developers to speak directly to machines as well as human users. To enhance this process, generative engine optimization (GEO) is becoming increasingly important. Clear, well-defined data, articulate endpoint descriptions, parameter explanations, code snippets, sample calls, and real-world use cases all aid GEO as they provide context for picking the right API and improving understandability. llms.txt, an emerging standard similar to robots.txt but for AI, is becoming more useful for discovery, as it tells an LLM exactly what to look for instead of assessing each site path and making its best guess.

Improving API discoverability helps guarantee that the LLM always gets the most up-to-date information and data. It’s also a vital component of retrieval-augmentation generation (RAG), which makes good API documentation doubly vital as it allows AI to discover internal APIs as well as public ones, and supply the generation layer with accurate, relevant details."

https://nordicapis.com/how-to-optimize-api-documentation-for-ai-discoverability/

#APIs #APIDocumentation #GEO #APIDiscoverability #RAG #LLMs #AI #GenerativeAI #Metadata #TechnicalWriting #SoftwareDocumentation

"[W]henever I need a Redocly or Swagger UI CLI command, again, I use ChatGPT. And it does something the site doesn’t do in that I get completed commands ready to run. Easier, faster, and more productive than going to your site. Writers may not do this a lot themselves. Developers do. This is where thinking like a developer comes in. They use AI for programming. Don’t take my word for it. Ask around. Watch developers work. Write an application yourself, something I advocate API documentation writers must do anyhow. These suggestions are for seeing how AI is creeping into the developer’s work style.

That means developer don’t know or care what the site looks like. Only that the content is there. The key is that AI doesn’t make up information. It gets it from somewhere. And that somewhere is your site. The more complete the information, field explanations (remember this one from earlier?), and interactions, the more complete the AI can be. That means happier developers.

In that regard, writers will always have a job writing content. Writers won’t always have a job formatting the output or publishing files. Your publishing tools are needed for the moment. But you need to be thinking two to five years in advance. Now."

https://robertdelwood.medium.com/writing-api-documentation-for-ai-and-not-developers-d245f8b97c18

#TechnicalWriting #APIs #APIDocumentation #SoftwareDevelopment #AI #GenerativeAI #Programming

"Finally, I haven’t even pitched the strongest argument for why technical writers and documentation will continue to be relevant in the future: AI tools are terrible without good documentation. In the same way that you need valid, accurate context when using AI tools to create documentation, AI tools need an accurate body of documentation to produce useful, hallucination-free outputs. Informal tests by my colleagues show that AI outputs improve by orders of magnitude when trained on more abundant and accurate documentation.
(...)
In other words, technical writers will create and package information specifically for AI consumption, ensuring the AI has the necessary context to produce accurate and relevant results.

There’s a sales motive for keeping technical writers around, too. Let’s say an external developer needs to create, say, a mapping application for their project, and they decide they need routing logic. Following a vibecoding approach, they integrate your company’s MCP server into their IDE and tell their AI tool to create an app that draws routes from one point to another. If the AI tool can successfully fulfill the developer’s needs, requiring only that they provide an API key (which then initiates billing), the company that has provided this solution will sell more API services. No one wants to fiddle and fuss with hard-to-configure technology that doesn’t work, and by hard-to-configure, I mean APIs that require manual configuration rather than APIs you can configure with natural language."

https://idratherbewriting.com/blog/strategies-to-succeed-in-context-of-ai

#TechnicalWriting #TechnicalCommunication #AI #GenerativeAI #SoftwareDocumentation ##SoftwareDevelopment #APIDocumentation

"A documentation platform is a product that provides capabilities—some free; some paid—for a range of activities, like authoring, editing, collaborating, monitoring, building, deploying, and publishing documentation.

Docs-as-code platforms are more common these days, as more people can code or leverage systems like AI that help them code. Traditionally, authoring to publication might take place locally, or via a software product that offered manual versioning, limited collaboration, and limited-to-no support for documentation pipeline automation, like setting up a CI/CD pipeline to deploy health documentation when the `main` has a new commit. If you remember the yesteryears of authoring, products like Subversion and TortoiseSVN may come to mind.

The platforms I’m writing about today are modern and all offer some type of free documentation generation, usually through static site generation; however, outside of what’s provided out of the box, there are notable differences between what’s provided for free and what’s provided at cost.

My goal for this article is to point out some core differences across add-ons, features, and enterprise-level support across these documentation platforms so that you choose the platform that’s best for you and your documentation readers.

For this article, I researched Fern, Mintlify, ReadMe, and Redocly."

https://www.copytree.io/post/choosing-modern-docs-platform

#TechnicalWriting #SoftwareDocumentation #APIDocumentation #APIs #SoftwareDevelopment #Programming #DocsAsCode

"In our piece exploring whether the AI revolution is leaving APIs behind, we wrote about some of the factors limiting the extent to which AI tools like chatbots can interface with APIs.

Some of these include:

- Limited or no access to APIs for developers
- APIs are sometimes overcomplicated, bloated, or difficult to call
- Legacy APIs (WS/RPC) lack thorough or up-to-date documentation
- APIs sometimes only cover a fraction of the functions available via the UI

It’s worth noting that many of these points impact human API consumers just as much as they do agentic ones. If you’ve ever been in the position of trying to use an API and it falling short of your expectations, you’ll know just how frustrating it can be.

While it’s possible that some of those users will get in touch to ask you to add certain endpoints or clarify things, plenty more won’t. Some developers are more likely to take the view that it’s easier to ask for forgiveness later than permission now, and find some other way to extract the data they’re looking for. In many cases, web scraping offers just such a solution.

Web scraping APIs are a natural evolution of manual scraping techniques, such as using Python to scrape websites. Used for everything from scraping search engine results, like SERP APIs, to product prices and sentiment analysis, there are various services out there that make web scraping very straightforward. And they’re big business."

https://nordicapis.com/are-web-scraping-tools-overtaking-official-apis/

#APIs #WebScraping #SoftwareDevelopment #Programming #APIDocumentation #APIDesign #Python

"API documentation writers don’t just write content. We’re liaisons between client developers and in-house developers. I often say “we’re paid by the company but work for our clients.” Many think that in-house developers automatically empathize with the client developers. After all, they’re all developers, right? Right? Well, no. A surprising number of times, in-house developers are actually out of touch with clients. Why else would we be talking about having clear field names? They get tunnel vision or become myopic while in the code. This is not unique to developers. All professions have this risk. That’s our job to make sure that clarity is there for the clients. We can’t do it completely by ourselves. We need developer’s buy in. That means, one of two things.

We can push back on the in-house developers. When we see a meaningless, poor, or bad field names, for example, we have the right, if not obligation, to get it changed. Some developers may disagree. That’s OK. The truth is, the code doesn’t belong exclusively to in-house developers. It’s the client’s code. They’re the ones intended to run the code, to know which fields to pass in, with which values, and to read the response JSON. That makes it our code, too. We not only have to run the code but also to explain this to clients. We have a say in the matter."

https://robertdelwood.medium.com/writing-for-humans-an-api-documentation-writer-writes-3c51a6ea87a5

#TechnicalWriting #APIs #API #APIDocumentation #SoftwareDevelopment #APIs #TechnicalCommunication