Category: Developer Docs

Debunking Myths About Technical Writing for Developers

In my years as a technical writer at Google, Grafana Labs, and Microsoft, I’ve encountered countless misconceptions about what technical writers actually do. Some stakeholders view us as glorified clerical support. Others expect us to be senior-level software engineers who can independently build production systems while maintaining documentation.

The reality is far more nuanced: we are specialized bridge-builders who manage the high-stakes intersection of human language and machine logic. We operate in the gap between what engineers know and what developers need to learn, transforming tribal knowledge into scalable education.

Here are the most common myths I’ve encountered about technical writing, what the role actually entails, and why getting this right matters for your product’s success.

Myth 1: Technical Writers Are “Information Secretaries”

The Misconception: Technical writers wait for engineers to send over notes, then simply reformat those notes into documentation. We’re basically human content management systems.

The Reality: This “secretary” approach produces fragmented, contradictory, and low-quality content. Real technical writing involves investigative research, information architecture, audience analysis, and strategic content design.

What the Work Actually Looks Like

Professional technical writers don’t passively wait for information. Instead, we:

  • Embed ourselves in development cycles, attending sprint planning and standups to understand what’s being built and why
  • Conduct structured interviews with SMEs, asking targeted questions that uncover not just what features do, but why they exist and how they fit into the broader ecosystem
  • Analyze documentation gaps by reviewing support tickets and user feedback to identify where developers actually struggle
  • Create information architectures that map content to user journeys, ensuring developers find answers through intuitive navigation
  • Establish content standards that ensure consistency across teams

Good technical writers architect knowledge systems that serve multiple audiences simultaneously. This is why asking engineers to “write down what they built” produces documentation with no coherent learning path.

Myth 2: The Technical Writer Must Write Every Line of Code

The Misconception: A good technical writer should independently write all code samples, from basic examples to complex production implementations.

The Reality: While I maintain working knowledge of some languages, enterprise software is often too complex for a single writer to generate all code samples. Instead, my role is better described as a Technical Director who orchestrates high-quality examples.

The Technical Director Model

1. Identify the Use Case

Engineers want to document what they built; users need to understand how to solve problems. I translate between these perspectives:

  • Engineer thinking: “We added support for custom retry policies”
  • User thinking: “How do I make my API calls more resilient to network failures?”

2. Enlist the Right Resources

I identify which engineer has the deepest knowledge of a subsystem, who writes clean example code, and who has bandwidth to contribute.

3. Verify, Refine, and Test

Raw code from engineers rarely ships as-is. My review includes:

  • Compilation and execution testing: Does it actually work?
  • Security review: Are we showing unsafe patterns?
  • Readability optimization: Removing complexity, adding explanatory comments
  • Error handling: Ensuring examples show realistic error handling, not just happy paths

4. Maintain and Update

I track which code samples are affected by breaking changes, coordinate updates before deprecated features are removed, and maintain automated testing for critical examples.

When Technical Writers Do Write Code

For simpler examples, I may write the code myself. But attempting to write complex, production-representative code for unfamiliar systems wastes time and produces fragile examples.

Myth 3: Engineers Can Simply Replace Technical Writers

The Misconception: Documentation is just “writing stuff down.” Any engineer with decent communication skills can handle it, eliminating the need for dedicated technical writers.

The Reality: While some engineers are excellent writers, systematically replacing technical writers introduces profound risks. This isn’t about engineers lacking capability—it’s about specialization, incentives, and the invisible complexity of documentation as a discipline.

The Commitment Gap

An engineer’s primary work is building features and fixing bugs. Documentation becomes a secondary concern, creating predictable patterns:

  • Documentation written only when forced by PR requirements
  • Stale documentation when APIs change but docs don’t get updated
  • Inconsistent styles across different engineers’ contributions
  • Coverage gaps where exciting features get documented but essential operational concerns don’t

The Curse of Knowledge

Engineers suffer from unconsciously assuming others share their background knowledge. Here’s what this looks like:

Engineer-written documentation:

Configure the service mesh egress gateway to handle external traffic.

Technical writer-written documentation:

Before your application can make requests to external APIs, you need to configure the service mesh to allow outbound traffic. In Joeware, this requires creating an Egress Gateway.
Prerequisites:
- Joeware installed in your cluster (see Installation Guide)
- kubectl configured to access your cluster
- Basic familiarity with Kubernetes Services (see Kubernetes Primer)
To configure external traffic...

The engineer’s version isn’t wrong, but it assumes you know what a service mesh is, what “egress” means, and where configuration happens. Technical writers are trained to identify implicit assumptions, sequence information to build from foundational concepts, and anticipate failure modes.

The Reality: A Partnership of Specialists

The most effective documentation emerges from genuine partnerships where:

  • Engineers provide deep technical expertise about how systems work
  • Technical writers provide audience expertise about how developers learn and where they struggle
  • Both groups review each other’s work to catch technical errors and pedagogical gaps

When documentation is treated as a first-class citizen of the engineering process, organizations see measurable results:

  • Lower support costs: Developers self-serve answers
  • Faster onboarding: New users become productive in hours instead of days
  • Better product decisions: Explaining features often reveals UX problems before launch
  • Competitive advantage: Documentation quality often determines which product wins

What Good Looks Like

Technical writers aren’t a luxury. We’re strategic investments in product success, developer experience, and sustainable growth. The best documentation teaches developers to think in your product’s paradigm, anticipates their struggles, and makes the complex feel achievable.

That’s not something you get by asking engineers to write more clearly. It’s something you get by respecting technical writing as the specialized craft it is.

The “Docs-as-Code” Transition: Moving Beyond the CMS

For years, the standard for technical documentation was the monolithic CMS systems designed for “content” in the abstract, but often divorced from the actual environment where software is built. My journey across organizations like Google, Microsoft, and Grafana Labs has fundamentally shifted my perspective toward docs-as-code workflows.

Why the Shift?

Early in my career, I saw the friction caused by siloed documentation. When docs live in a separate web portal managed by a non-developer editor, they naturally drift away from the source code. By adopting tools like Git, Markdown, Hugo, and Docusaurus, we bring documentation into the developer’s native habitat. This allows developers to take responsibility for documenting their own work, and it allows technical writers to be more fully integrated into the team’s development process.

For technical writers transitioning from traditional CMS platforms, this shift represents a fundamental reimagining of your role within the development team. You’re no longer the downstream recipient of incomplete information; you become an embedded collaborator who can see, understand, and influence the code alongside the documentation. This visibility transforms the quality and accuracy of what you produce.

Treating documentation like code means it follows the same lifecycle as the product:

Version Control: Using Git allows for precise tracking of changes and the ability to revert errors instantly. Beyond basic rollback capabilities, Git enables powerful branching strategies where documentation updates can be developed in parallel with features, tested in staging environments, and merged only when the feature ships. This synchronization prevents the common problem of documentation being published too early or too late relative to feature releases.

Peer Review: At Google and elsewhere, I submitted substantial change-lists or pull-requests (PRs), ensuring every word was vetted by engineers through the same code review process they use for features. This peer review culture catches technical inaccuracies before publication and creates shared ownership of documentation quality. Engineers become invested stakeholders rather than reluctant contributors. The review process also serves as an informal mentorship opportunity whereby junior engineers learn from seeing how senior developers critique and improve documentation, while writers gain deeper technical insights through reviewer feedback.

Automation: CI/CD pipelines can run linters to check for broken links or style guide violations before a single page is published. At Grafana Labs, we used linters to check for divergences from the team’s Writers’ Toolkit (our style guide). Advanced teams integrate Vale or other prose linters to enforce terminology consistency, readability metrics, and brand voice guidelines. Some organizations even run automated accessibility checks, ensuring documentation meets Web Content Accessibility Guidelines (WCAG) standards before deployment. This automation amplifies editorial judgment by catching mechanical errors that would otherwise consume review cycles.

Impact on Developer Experience

At Grafana Labs, I collaborated with a director of development and another engineer to lead the building of a developer documentation portal (grafana.com/developers) designed for discoverability. At the open-source observability company, engineers had built several distinct silos of information for their separate projects, but this made it frustrating to find what you needed. The company’s divergent threads for plugin building, specifications, and design system were brought together under one umbrella. You can read all about our journey to creating the portal at “The Grafana developer portal: your gateway to enhanced plugin development.”

When documentation lives in the repository, it becomes more of a “living” entity. Paid developers and open-source contributors alike are more likely to contribute updates or suggest edits when they can simply open a PR. The psychological barrier to contribution drops precipitously when the workflow mirrors what developers already do dozens of times per day.

Moreover, having documentation in the repository enables powerful cross-referencing. Code comments can link directly to documentation sections; documentation can reference specific lines of code with permanent links that update as the codebase evolves. This bidirectional relationship creates a cohesive knowledge ecosystem rather than two separate information silos.

Conclusion

The transition is about a transformation of documentation culture. It’s an acknowledgment that documentation is a first-class citizen of the software development life cycle (SDLC). When we treat docs like code, we bring software engineers and writers together to build better products. This cultural shift manifests in tangible ways: documentation tickets appear in the same sprint planning as feature work, documentation coverage becomes a release criterion, and engineers budget time for documentation the same way they budget for testing.

In the past 7 years since I first started using the docs-as-code workflow, I’ve learned that it isn’t just about version control or static site generators. It’s about breaking down the artificial barriers between code and documentation, between engineers and writers, between the product and its explanation. When those barriers dissolve, both the code and the docs improve, creating a virtuous cycle that benefits everyone: developers, writers, and most importantly, the users trying to understand and use what we’ve built.