Technical writing best practices when AI makes writing cheap but reading expensive

Table of contents

• The inversion: writing got cheap, reading got expensive
• Cheap writing is not neutral: AI bloats
• Why reading is now the leadership skill that matters
• The disengagement cost: when readers stop reading
• Poor readability is a cost center, not a soft problem
• How to measure readability: Flesch and Flesch-Kincaid
• Technical writing best practices: the three Cs
• How content engineering scales clarity instead of volume
• Conclusion
• Work with Weesho Lapara
• Additional resources

Technical writing best practices used to be about helping writers work faster. Now they are about engineering content so readers can actually absorb it. If you have spent the past two years watching your organization’s documentation volume climb while the actual usefulness of that documentation stays flat, you are seeing the same dynamic play out across every software company right now. AI has made generating text nearly frictionless, and the instinct is to treat that as a productivity win. But the economics have inverted: producing text is no longer the expensive act. Reading it, verifying it, and actually absorbing it are.

The inversion: writing got cheap, reading got expensive

A writer sitting in front of a large language model can produce a 2,000-word technical explainer in minutes. That is genuinely useful. But the reader on the other end still takes the same amount of time to read 2,000 words as they always did, and their attention is now shared across a much larger total surface of text, much of it generated at similar speed by everyone else.

Reports including those cited by Axios indicate a substantial increase in AI-generated content online, often making it difficult for readers to distinguish from human-authored text. Many studies suggest readers value clarity and accuracy regardless of the source. The production bottleneck has moved. The scarce resource is no longer words. It is the reader’s willingness and ability to engage.

For technical and engineering leaders, the consequence is concrete. Every runbook, design doc, API reference, onboarding guide, or internal knowledge base article that nobody reads is not a neutral event. It is a failed communication, and failed communication has a cost you can model. The discipline that used to sit in your docs team now needs to sit at the level of engineering systems: how do you build content that actually gets read?

Cheap writing is not neutral: AI bloats

The second-order problem is not just volume. It is length without substance.

AI models have a well-documented tendency to pad output. They circle the same idea, restate a point with slightly different phrasing, and fill space with hedges and transitions that carry no information. This is not a bug in the model’s reasoning; it is a predictable property of next-token prediction. Restating a concept you just established is the statistically safe continuation. That is why volume-first AI content strategies that ignore reading cost consistently produce documents that are longer than they need to be and less precise than they look.

The problem compounds with length itself. As AI-generated articles grow longer, they drift. Earlier points get restated, not developed. A two-paragraph insight becomes a twelve-paragraph section that says the same thing four times. Word-count inflation is a documented behavioral pattern across AI writing tools, with tasks consuming measurably more output than they used to for equivalent work.

The floor on quality is set by the source material. Generic inputs produce generic outputs: text that sounds correct, scans as authoritative, and says very little. If your knowledge base is shallow, your AI-assisted docs will be polished and shallow. If your source docs are already imprecise, the AI-assisted version will be imprecise with better sentence flow. The model cannot synthesize what it was not given.

Why reading is now the leadership skill that matters

Leaders have always needed to read critically, but the AI era makes that skill load-bearing in a new way. Confident-sounding output is now abundant. The signal that used to distinguish a carefully reasoned document from a hasty one, namely its length and surface polish, no longer carries that meaning.

The risk is that summarized, fluent, plausible-looking content gets accepted at face value. A design doc generated with an LLM and reviewed quickly by someone who trusts the prose style will sail through without the hard thinking that design docs exist to force. A strategy summary produced by AI and skimmed in a Slack thread may feel like alignment when it is actually ambiguity in polished clothing.

Deep reading is the skill that catches this. Not reading faster. Reading with enough attention to notice when something sounds right but has no actual claim behind it, when a recommendation is presented without a tradeoff, or when a document answers a different question than the one you asked. That is a discipline, and it is exactly the discipline that gets squeezed when document volume rises and meeting loads stay constant.

The disengagement cost: when readers stop reading

When inflated, generic content floods every channel, readers adapt. They skim. They send the doc to an AI summarizer and read the bullet points. They ask a colleague instead of checking the knowledge base. These are rational responses to a bad information environment.

The problem is that skimming is not reading at all. Eye-tracking studies on clinical and technical documents show that comprehension is measurably reduced during skim-reading, with content missed or only superficially processed. When a reader skims your incident post-mortem or your API integration guide, they are not absorbing the information at reduced efficiency. They are largely not absorbing it.

Research on AI-generated text is striking. Studies using eye-tracking show that AI-generated text is easier to skim but holds attention less well than human-written text, and that close reading of AI-generated content produces higher cognitive load, as measured by pupil dilation. The text that feels smooth to scan is, in many cases, the most effortful to actually understand. For API reference documentation, where poor readability directly stalls developer adoption, this is not an abstract problem. It shows up in support tickets, in delayed integrations, and in developers who give up and use a competitor’s SDK instead.

The instinct in some organizations is to compensate by moving important communication back to meetings and verbal channels. That instinct is right about the symptom and wrong about the cure. The fix is not more meetings. It is written content worth reading.

Poor readability is a cost center, not a soft problem

Documentation quality has always been difficult to put a dollar figure on, which is why it rarely appears in engineering budgets with the same weight as infrastructure or headcount. But the cost of poor documentation is real and measurable.

Every hour an engineer spends searching for an answer that should be in the docs, every support ticket raised because onboarding content was unclear, every delayed integration because an API reference was ambiguous: these are productivity losses you can model as a line item. IBM research has estimated the cost of unclear requirements and rework in software development at between 40 and 50 percent of total project cost. The Nielsen Norman Group has documented that users who cannot find or understand information in documentation turn immediately to support channels, which carry a direct service cost.

Poor technical writing is not a soft problem that lives in the quality-of-life category. It is an operating expense with a rate you can calculate. When you add AI-driven volume increases to the equation, the cost scales with the volume unless you build systems that enforce quality at the same rate you increase output.

How to measure readability: Flesch and Flesch-Kincaid

Readability is not subjective if you measure it. The two most widely used formulas for English-language content are the Flesch Reading Ease score and the Flesch-Kincaid Grade Level.

Flesch Reading Ease runs on a 0 to 100 scale. Higher scores mean easier reading. A score of 60 to 70 is generally considered accessible to a broad adult audience. A score below 30 indicates text that most readers will find difficult.

Flesch-Kincaid Grade Level maps reading difficulty to the US school grade level needed to understand the text. An 8 is readable by someone at an eighth-grade reading level. A 14 is readable at a university level.

The common mistake is treating lower grade levels as the target. That is the wrong goal for technical audiences. A documentation model built around reader needs recognizes that a senior engineer reading a protocol specification is not served by content written at a grade-6 level. The goal is matching complexity to the audience, not minimizing it. A well-written API reference will legitimately score at a higher grade level than a marketing landing page. What readability scores help you catch is unnecessary complexity layered on top of necessary complexity: sentences that are long because they are convoluted, not because the concept is hard.

Run these scores on your existing documentation. The results will tell you whether your complexity is earned or accidental.

Technical writing best practices: the three Cs

Technical writing best practices reduce to three properties that are concrete enough to enforce systematically:

Concise. Say what needs to be said and stop. This directly counters AI bloat. If a section can be cut by a third without losing a claim, cut it. Optimize for depth and relevance, not word count. A 300-word section that answers the reader’s question fully is better than a 900-word section that answers it and then restates it twice.

Complete. Concision does not mean incomplete. Every claim needs its context. Every procedure needs its prerequisites. Every configuration option needs its consequences. Terse writing that omits necessary information fails the reader just as surely as padded writing that buries it.

Consistent. Use the same term for the same thing throughout a document, across a documentation set, and across your product interface. Terminology drift is one of the most common failure modes in technical content and one of the most systematic to fix. A controlled glossary, maintained as a source of truth and enforced in review, eliminates an entire class of reader confusion. The essential content types for every software business all benefit from this discipline, but it matters most in reference material, where a reader will often jump in at any point and has no surrounding context to disambiguate terms.

These three properties are enforceable. They are not matters of taste. You can audit a document against each one and produce a specific, actionable list of failures. That is what makes them suitable for systematizing.

How content engineering scales clarity instead of volume

Content engineering is the discipline of building systems that produce clear content at scale, rather than relying on individual writers to apply quality at the end of each draft. The distinction matters when your output volume is rising faster than your editorial capacity, which describes most software organizations in 2025.

The systems layer includes:

• Style enforcement via prose linters. Tools like Vale and Proselint can be configured to catch bloat, flag banned phrases, enforce terminology from your glossary, and fail a pull request when content violates readability rules. The post on prose linting tools that enforce readability rules in CI covers the tooling options in detail. The key point here is that linting moves quality enforcement from a manual review step to an automated gate, which means it scales with your output rate rather than falling behind it.

• Structured content models. Structured documentation, where each content type has a defined schema and each schema encodes decisions about what information belongs where, reduces the surface area for drift and inconsistency. A structured how-to guide cannot accidentally become a reference article if the template does not have fields for reference content.

• Workflow design. Quality gates, review checklists, and defined ownership at each stage of a content workflow are what prevent poor content from shipping, not what catch it after the fact. The workflow is where you encode your standards as process rather than hoping writers remember them under deadline pressure.

• Readability measurement integrated into tooling. Flesch-Kincaid scores are computable automatically. Build them into your content pipeline and surface them alongside word count so writers see readability as a first-class metric.

This is not about slowing down production. It is about making the systems that support production as rigorous as the systems you already use for code. You do not ship code without tests. You should not ship documentation without equivalent gates. For teams thinking about structuring documentation for both human comprehension and AI consumption, structured content and enforced readability standards are what make that dual-audience requirement achievable at scale.

Conclusion

Technical writing best practices have always been about the reader, but AI has sharpened the stakes. When producing text is nearly free, the expensive act is reading it, which means every unnecessary word, every inflated section, and every inconsistent term is a direct tax on your readers’ time and your organization’s information flow. Readability is measurable. Clarity is enforceable. Content engineering is the discipline that makes both scale.

If your documentation volume is rising and your reader engagement is not, the answer is not more words. It is better systems for the ones you already have.

Work with Weesho Lapara

We build content engineering systems for software businesses: prose linting in CI, structured documentation models, readability audits, and writing standards your team can actually maintain. If you want to measure and improve the readability of your technical content, book a consultation or get in touch to talk through your documentation stack.

Additional resources

• Flesch Reading Ease and the Flesch-Kincaid Grade Level (Readable) — a clear explanation of both formulas, their scoring ranges, and how to apply them.
• Flesch-Kincaid Grade Level: Enhancing Document Clarity (ClickHelp) — covers how technical documentation may warrant higher grade levels matched to its audience.
• What Is Content Engineering? (Content Science Review) — foundational definition of content engineering as systems thinking and structured governance rather than one-off writing.
• Do Not Assume AI-Generated Text Is Interchangeable with Human Writing (ScienceNews.dk) — summarizes eye-tracking research on how readers engage differently with AI-generated versus human-written text.
• Content Bloat vs Content Strategy: Why AI Isn’t Enough (DMA Comms) — practical argument for why generative AI volume without editorial strategy produces content that sounds correct but says very little.