Designing Content for AI Summaries: A Practical Guide for Communicators

There’s a certain irony in admitting this, but I recently struggled to write the introduction to one of my blog posts, “Agent vs Agency in GenAI Adoption: Framing Ethical Governance.” I wanted to frame the topic with a reflection on evolving terminology, a nod to Hamlet, and a meditation on AI’s “nature.” On top of that, I introduced the idea of the “ghost in the machine” only a few paragraphs later. In hindsight, I had written two introductions to the same post without meaning to.

At the time, the ideas felt connected. But when I later ran those paragraphs through an AI summarizer, the summary focused almost entirely on Hamlet’s moral dilemma and the mind–body problem—interesting concepts, certainly, but hardly the point of the post. The AI confidently reported that the blog was “about comparing the adoption of GenAI to Hamlet’s struggle with death.”

Not exactly the message I intended.

To be fair here, the most recent version of Google’s Gemini gave me a much more comprehensive summary. That summary mentions, as I did, “the tensions inherent in adopting Generative AI” and my proposed “governance framework.”

But looking back, I realize I had made two classic mistakes in writing that introduction—mistakes that human readers can forgive with patience but AI summarizers absolutely cannot. First, I opened with a metaphor instead of a clear point. Second, I layered multiple conceptual frameworks (terminology, nature vs. nurture, Hamlet, Koestler, agency) before stating my purpose. I know better. Many of us do. But as I’ve written elsewhere, expertise doesn’t exempt us from the structural pitfalls that now matter more than ever.

That experience became the seed of this post.

If our writing can be so easily misinterpreted by a summarizer—and thus by downstream readers who rely on that summary—then it’s worth rethinking what it means to write clearly and responsibly in an AI-influenced world. Good writing has always been about serving our readers. Now, increasingly, it must also serve the machine readers that bridge the gap between our content and those readers.

In this post, I explore why AI summarizers can distort meaning, how machines “read” what we write, and how we can design content that preserves accuracy, nuance, and intent—even after it’s digested by AI. (Note: Some content in this blog post was generated by ChatGPT.)

Why AI Summaries Distort Meaning

When humans skim, we glide across the surface of the text, tugging on meaning as we go. We skip sentences, jump back when we lose the thread, and fill in gaps using context or background knowledge. AI can’t do any of this.

How AI Summarizers Work (or Not)

“AI reads your first sentence literally,” explains Tony M. in his article “How to Write for AI Summarization.“ He defines AI summarization as “a process by which artificial intelligence, usually large language models (LLMs), reduces longer content into a concise overview.” AI summarizers, whether they are extracting from or abstracting your article (or both), rely heavily on headings, topic sentences, and repeated terms to infer hierarchy and importance.

As student guidelines at Monhash University caution us, “GenAI does not interpret the text; rather, it looks for particular words that are placed together, and its responses are based on this.” (See “Effectively summarizing with generative artificial intelligence.”)

Specifically, AI summarizers struggle in the following ways. They:

• Collapse nuance into generalities.
• Lose conditional statements (“only if,” “except when”).
• Over-prioritize opening sentences—even those that say very little.
• Mistake proximity for importance.
• Cannot distinguish essential information from interesting details.

Harking back to my graduate school days, I recognize in these summarizers a tendency to act more like a mechanical reader or grader. Not something I relied on when I graded freshman essays. But wow, the literalism is familiar.

Literalism and Ethical Considerations

This literalism matters because, as I argued in my July 2025 blog post “Ethical Use of GenAI: 10 Principles for Technical Communicators,” accuracy and clarity are not optional virtues. They are ethical responsibilities, especially in high-risk contexts such as compliance, safety, and finance.

AI reads like a strict outline grader, not like a human.

Ethical considerations with the use and proliferation of AI summarizers run wide and deep. A list from Doc-e.ai includes everything from erosion of deep understanding to biases and a lack of accountability for errors.

If a summary distorts our meaning, we risk undermining that responsibility and creating a new vector for misinformation—even when the original content is excellent.

What Communicators Need to Know About AI Summarization

Summaries increasingly serve as the first point of contact between your writing and your audience. AI-generated snippets appear in search results, enterprise tools, autogenerated briefs, and even internal communications platforms. When a summarizer misinterprets an article, it isn’t just an inconvenience—it’s a distribution problem.

(Note: Depending on the extent of the AI summarizer’s “knock off,” it might also be a copyright problem. See Natalie Wexler’s cautionary tale on the website Minding the Gap.)

Faults in AI-generated summaries of our articles, white papers, blog posts, and similar long-form writing show up in the following ways:

• Dropped conditions: Constraints and exceptions vanish.
• Shifted emphasis: A minor detail becomes the headline.
• Compressed nuance: Complex ideas are flattened.
• Mistaken relationship: Concept proximity assumes a relationship. Related ideas are confused.
• Lost purpose: The summarizer captures feedback or examples but misses the main point.

Understanding these patterns helps us anticipate how our writing might be reshaped once it leaves our hands.

Seven Strategies to Improve AI-Resilient Writing

Below are seven practical strategies to help your writing hold up online, whether read directly by humans or interpreted first by an AI summarizer.

1. Lead with your meaning—always.

AI (and most humans) assume your first sentence defines the paragraph’s purpose. Create strong topic sentences. Put your key point up front; add nuance after. (Don’t bury the lede!)

2. Use clear, descriptive headings.

Headings should convey information, not merely label a section. “How the New Process Reduces Errors” gives machines and readers far more guidance than “Overview.” Those of us who are former technical communicators learned this rule quickly the first time we had to construct a table of contents for a long-form manual.

Also, ensure that the hierarchical relationships among headings are clear. Help the reader (human or machine) build a mental model of your content.

3. Chunk information into single-purpose paragraphs.

Chunking—something I explored in my 2024 blog post on accessible content—helps both humans and machines avoid cognitive overload. Each paragraph should advance one idea.

To take this concept a step further, consider borrowing techniques from technical communicators to modularize content, ensuring that each subsection or paragraph is complete enough to stand on its own.

4. Add explicit transitions.

Machines cannot infer logical connections. Signal contrasts, conditions, and consequences with phrases such as “However…,” “In contrast…,” or “This means…”

5. Surface conditions and constraints early.

Summarizers often drop conditional language and qualifiers when they appear late in the sentence. If the condition is important, place it near the beginning of the sentence or paragraph. (<- See this last sentence for an example!)

6. Reinforce important concepts through repetition.

Repetition is not redundancy—it’s reinforcement. Machines rely on term frequency to determine relevance. Consider following the favorite consistency principle of technical writers to limit a single meaning to a single term or phrase.

7. Keep sentences tight and hierarchy clear.

Long, branching sentences introduce ambiguity. Choose clarity over elegance when you must choose. Break apart those clause- and phrase-heavy sentences.

Extra Note: Use Alt text and captions with images and videos.

AI summarizers cannot read or summarize images or videos. Use verbal cues in the text of your article to integrate these elements. At the very least, leverage Alt text and captions to label your non-text items.

Tony M., in his article “How to Write for AI Summarization,” provides additional tips for mixed content types.

Examples of AI Summary Errors (and How to Fix Them)

The hypothetical examples below show how common AI summarization errors arise—and how small changes can preserve your meaning.

Example 1: When AI Drops Critical Conditions

Original Paragraph:
“Our new data-retention policy standardizes how long departments must keep operational files. Most teams will need to store records for seven years, though a few teams—particularly those handling health or financial data—must retain their records indefinitely. The policy also encourages departments to review and purge redundant materials annually.”

Typical AI Summary:
“The new policy requires departments to keep operational files for seven years and encourages annual purges.”

AI-Resilient Revision:
“The new data-retention policy standardizes how long departments must store operational files. Most teams must keep records for seven years. However, teams that handle health or financial data must retain those records indefinitely. The policy also encourages all departments to review and purge redundant materials each year.”

AI Summary of the Revision:
“Most teams must keep records for seven years, while health and financial data must be kept indefinitely; all departments should review and purge redundant materials annually.”

Why It Works:
The conditional requirement—introduced now by “However”—cannot be lost or misinterpreted.

Example 2: When AI Elevates the Wrong Message

Original Paragraph:
“Our customer support team will begin using a new triage model in January. The biggest improvement is that customers with urgent issues will be connected to a specialist faster. Additional benefits include reduced wait times for general questions and a new callback option. However, teams must first complete a mandatory two-hour training before the new workflow goes live.”

Typical AI Summary:
“The new triage model requires a two-hour training session.”

AI-Resilient Revision:
“Our customer support team will begin using a new triage model in January to improve service for customers with urgent issues. The model connects high-priority callers to specialists more quickly and reduces wait times for general questions. It also introduces a new callback option. Before the workflow goes live, all teams must complete a mandatory two-hour training.”

AI Summary of the Revision:
“The new triage model will improve service for urgent issues, reduce wait times, add a callback option, and require a two-hour training session.”

Why It Works:
The summary now mirrors the intended hierarchy: customer benefits first, internal requirements second.

Example 3: When AI Loses the Purpose of the Content

Original Paragraph:
“The new mobile app lets employees submit expenses, check claim status, and upload receipts. Early testers appreciated the simplified interface but requested clearer instructions for international travel. They also identified a syncing delay between the app and the desktop dashboard, which our development team is addressing.”

Typical AI Summary:
“Testers appreciate the simplified interface but report syncing delays.”

AI-Resilient Revision:
“The new mobile app helps employees manage expenses by offering three key features: submitting claims, checking claim status, and uploading receipts. Early testers liked the simplified interface and requested clearer instructions for international travel. They also reported a syncing delay between the app and desktop dashboard, which our development team is addressing.”

AI Summary of the Revision:
“The app offers three main expense-management features; testers liked the interface, requested better travel instructions, and noted a syncing delay being addressed.”

Why It Works:
The summary finally includes the app’s purpose, not just the testers’ feedback.

Example 4: When AI Misreads Mixed Topics

Original Paragraph:
“Our quarterly audit revealed several opportunities to strengthen our internal review processes. Some project teams lacked clear documentation of decision rationales. Others struggled with inconsistent file naming or incomplete metadata. A few teams excelled in maintaining accessible, well-organized repositories. Based on these findings, we will conduct a short series of workshops in February to standardize practices.”

Typical AI Summary:
“The quarterly audit found inconsistent documentation.”

AI-Resilient Revision:
“Our quarterly audit revealed a mix of strengths and opportunities in our internal review processes. Some project teams lacked clear documentation of decision rationales, while others struggled with inconsistent file naming or incomplete metadata. A few teams maintained exemplary repositories with well-organized, accessible files. To support all teams, we will offer workshops in February to help standardize best practices.”

AI Summary of the Revision:
“The audit showed both strengths and weaknesses across teams, and workshops will be offered in February to standardize best practices.”

Why It Works:
The summary is now balanced and representative—not reduced to the first flaw.

Example 5: When AI Misinterprets Narrative Flow

Original Paragraph:
“During the pilot, several participants said the chatbot felt ‘helpful but hesitant.’ Users appreciated the accuracy of the answers but noted that multi-step tasks sometimes stalled. One participant even described the chatbot as ‘timid’ when asked clarifying questions. Despite these quirks, the pilot succeeded in reducing average response time by 32 percent.”

Typical AI Summary:
“Users felt the chatbot was hesitant and sometimes stalled.”

AI-Resilient Revision:
“During the pilot, the chatbot reduced average response time by 32 percent. Participants described the tool as accurate and generally helpful, though some noted that multi-step tasks occasionally stalled or felt ‘hesitant.’ These insights will inform the next iteration of the design.”

AI Summary of the Revision:
“The pilot reduced response time by 32 percent, and testers found the chatbot helpful overall, with some issues to address.”

Why It Works:
Leading with the quantitative result ensures it becomes the anchor of the summary.

How to Future-Proof Your Content for AI Tools

AI-mediated reading is becoming the norm, especially in Google search engine results. Summaries, answer engines, and embedded assistants increasingly present a shortened version of our work before readers ever see the full text. Future-proofing simply means designing content so that its meaning stays intact—for both types of audiences.

Here are three practical, low-effort ways to future-proof your writing.

1. Write for human and machine readers at the same time.

Both audiences benefit from:

• Clear hierarchy
• Single-purpose paragraphs
• Early placement of high-stakes information
• Predictable, consistent structure

If the structure makes sense to a summarizer, it will likely make sense to a busy human reader as well.

2. Add a light structural check to your workflow.

You don’t need a new process—just a brief step:

• Run a section through an AI summarizer to see whether your main point surfaces.
• Check whether the summary retains conditions and constraints.
• Ensure headings and topic sentences match what follows.

This “stress test” reveals ambiguity before your readers encounter it. Note that most GenAI tools, such as ChatGPT, offer summarization capabilities. Adobe Acrobat also has an AI-assisted summarizer.

3. Prepare for AI-first consumption.

Readers may encounter your content as a short snippet, a paragraph, or a generated list. Support these formats by:

• Leading with purpose and key outcomes
• Using explicit phrasing (“The policy requires…”)
• Making relationships clear (“X applies only when Y…”)

When meaning is anchored early and stated plainly, it is more likely to survive compression and remixing.

The Human X Factor in an AI-Summarized World

In my earlier blog post, “Leveling an Editorial Eye on AI,” I described the “X factor”—the deep contextual insight that only humans bring to content creation and review. It involves specialized and deep expertise, professional experience and judgment, and a conscientious perspective.

That X factor remains essential. Machines can help check mechanics and formatting; they cannot evaluate nuance, risk, or audience expectations.

This is why “writing for machines” is not the same as “writing like a machine.” It’s about designing content with enough clarity and structure that humans—and machines—can interpret it faithfully.

And as JoAnn Hackos has long reminded us, communicators must constantly evaluate and “define the level of quality to be achieved.” In the AI era, that includes designing content that can withstand machine interpretation.

Preserving Meaning in a Machine-Mediated Era

We’ve entered an era in which many readers may see a summary of our content before they ever see the content itself. This isn’t a reason to panic, but it is a reason to write with intention.

When we write for readers and machines, we aren’t lowering the bar—we’re raising it.

• We are safeguarding meaning.
• We are respecting our audiences.
• We are practicing our profession with the ethical care it deserves.

And in doing so, we ensure that our content continues to serve its purpose—accurately, responsibly, and conscientiously—no matter how it reaches its readers.

Photo by abhi shek on Unsplash