Category: Software Engineering

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.

Systems Theory: From Ecology to Software Architecture

When I tell people I studied environmental science and political ecology before becoming a technical writer, I usually get puzzled looks. What does understanding threatened ecosystems have to do with documenting APIs or managing AI-assisted workflows?

The answer is everything. Systems theory—the framework that helped me understand how governance impacts ecological resilience—has become one of the lenses through which I approach software architecture, documentation strategy, and AI integration.

Ecological Resilience vs. Software Reliability

In my early academic work, I focused on how human governance systems impact ecological stability. One core concept was resilience: an ecosystem’s ability to maintain essential functions despite external shocks. A resilient forest survives wildfire by regenerating from deep root systems; a resilient wetland processes pollution spikes without collapsing.

Software systems face analogous challenges. The industry has shifted from prioritizing “robustness” (i.e., building systems that resist failure) to embracing “resilience,” designing systems that fail gracefully and recover quickly.

Consider a monolithic application where a single memory leak brings down the entire system. In ecological terms, this is like an invasive species that monopolizes resources and crashes the entire food web. Just as technology can threaten ecological balance through unintended externalities, a poorly isolated “feature” in a monolithic architecture becomes a pollutant that degrades performance across the entire environment.

The solution in both domains is similar: create buffer zones and compartmentalization. Ecologists design wildlife corridors that contain localized disturbances. Software architects implement circuit breakers and microservices that isolate failures and prevent cascading collapses. When learning about software systems, I look for these isolation patterns as an architectural philosophy that protects system health.

Governance and Technical Debt

My background studying governance systems prepared me for understanding technical debt. In environmental management, rigid or poorly informed policies lead to catastrophic outcomes. For example, irrigation systems that create dead zones, or forestry policies that suppress natural fires until fuel loads become catastrophic.

Software has its own governance: the rules, standards, and conventions dictating how components interact. When this governance is rigid or uninformed, systems become brittle. When the “Expert-in-the-Loop”—the human with contextual understanding—is removed from critical decisions, you get the software equivalent of ecological collapse.

This is why I’m cautious about AI-generated code. An AI might confidently suggest deprecated methods or invent non-existent API parameters. Without adaptive governance involving rigorous verification and comprehensive documentation, these errors propagate like invasive species through an unmonitored ecosystem.

The parallel extends to how both systems accumulate debt. Environmental degradation results from short-term decisions that externalize costs to the future. Technical debt accumulates the same way: quick fixes that seem expedient but create compounding maintenance burdens. In both cases, governance determines whether those debts become manageable or catastrophic.

Documentation as Ecosystem Mapping

When I document software systems, I don’t catalog individual API endpoints in isolation. I map the ecosystem: how data flows, how components depend on each other, what feedback loops exist, where boundaries are defined. This is fundamentally a systems-theory approach to information architecture.

Just as an ecologist must understand why species thrive or decline within their context, I must extract the “why” behind architectural decisions. Why does this service retry failed requests? What upstream conditions make this endpoint vulnerable? How does this component fit into larger workflows users actually care about?

The Expert-in-the-Loop as Environmental Steward

Threats to ecosystems often stem from “blind” automation or inadequate oversight: industrial processes that externalize pollution and algorithms that optimize narrow metrics while degrading broader system health. I apply this cautionary principle to AI-assisted workflows.

The ultimate systemic skill—whether in ecology or software—is discernment. Knowing when to rely on automated efficiency and when human strategic thinking is non-negotiable. Understanding that systems are more than the sum of their parts, and that expertise means seeing the whole while attending to the details.

Systems theory gave me mental models to understand threatened ecosystems. Those same models now help me navigate the complex, interconnected world of modern software development. The vocabulary changes, but the fundamental patterns remain consistent.