In a world where AI is rapidly becoming the primary lens through which information is filtered, understanding how to communicate with these systems is no longer a niche skill—it’s a critical new frontier in digital marketing. We’re joined today by Anastasia Braitsik, a leading expert in SEO and content strategy, who specializes in a discipline that sounds like science fiction but is now a daily reality: engineering content to survive interpretation by large language models. We’ll explore the peculiar weaknesses of AI, such as its tendency to misinterpret the middle of long articles—a phenomenon she calls “dog-bone thinking.” Anastasia will break down why this happens and share powerful, structural strategies to ensure your most important messages aren’t lost in translation, from crafting resilient “answer blocks” to a five-step editing process that can fortify any piece of content for our new machine-driven world.
AI systems often struggle with the middle of long content, a phenomenon sometimes called “lost in the middle.” What are the primary technical reasons for this weakness, and how does aggressive content compression by modern AI systems make this problem even worse for content creators?
It’s a deeply frustrating experience to see your carefully researched work get mangled. The core of the problem is a double-hit on the middle of your content. First, research from places like Stanford has quantified what many of us have suspected: LLMs have an attention bias. Their performance is highest when key information is at the very beginning or the very end. Anything in the middle is in a danger zone, where the model is more likely to lose the thread. It’s like the model gets tired halfway through. Then, you layer on the second problem: aggressive, system-level compression. To control costs and keep workflows stable, systems are designed to prune and summarize long content before the model even sees it. The middle, which is often the easiest segment to collapse into a mushy summary, becomes the primary target. So your content is fighting to survive two filters that both attack the same place.
A recommended strategy to combat AI misinterpretation is using “answer blocks” in the middle of content. Can you describe the essential components of a strong answer block? Please provide a step-by-step example of how you would transform a standard prose paragraph into this more resilient format.
This is the key to making the middle “hard to summarize badly.” Most writers use the middle for nuance and connective prose, which is exactly what gets lost. An answer block is the opposite; it’s a dense, self-contained unit of information designed to survive on its own. It needs four essential components: a clear claim, a specific constraint, a supporting detail, and a direct implication. The absolute test is whether it could be quoted by itself and still make complete sense.
Imagine a standard paragraph: “Our new software helps businesses improve their customer service response times in several ways. By integrating with existing platforms and using an advanced algorithm, it can help teams address tickets more efficiently, which is important for maintaining customer satisfaction, especially in competitive markets.”
To transform this, you’d break it into a hard, quotable block:
Claim: Our software reduces customer service response times.
Constraint: For businesses using integrated helpdesk platforms.
Supporting Detail: It achieves this with an advanced algorithm that prioritizes tickets.
Implication: This leads to higher customer satisfaction in competitive markets.
Now, instead of a wandering thought, you have a solid brick of information that a machine can easily lift, understand, and cite correctly without losing the core meaning.
Let’s talk about structural anchors. How does periodically “re-keying” the main topic in the middle of a piece help an AI maintain focus? Similarly, why is keeping supporting evidence physically close to a claim so critical for how systems will interpret and cite your work?
Structural anchors are like signposts for a machine that can easily get lost. “Re-keying” is a crucial one. Right at the midpoint of your article, you need to insert a short paragraph—maybe just two to four sentences—that plainly restates your main thesis, the key concepts, and the primary decision criteria. It feels a bit repetitive to a human, but for the model, it’s a vital reset. It serves as continuity control, reminding the AI what the article is fundamentally about and preventing it from drifting. This also acts as a signal to compression algorithms, essentially telling them, “Hey, this part is important; don’t throw it away.”
Keeping proof local to the claim is just as critical. If you make a claim in paragraph 14 but the data supporting it is buried down in paragraph 37, you’re creating a huge risk. A compression system will almost certainly sever that link, summarizing the middle in a way that turns your evidence into disconnected mush. The model then sees a claim without proof and either ignores it or hallucinates the support. By placing the number, the date, the definition, or the citation right next to the claim, you create an unbreakable unit. This makes your content far easier for an AI to cite properly because it doesn’t have to stitch together context from multiple places.
While writers often vary terminology for stylistic reasons, this can create “fog” for AI. How does using consistent naming for key concepts improve machine comprehension? Please also explain how using structured formats, like lists or definitions, makes your content more valuable for AI systems.
This is a subtle but powerful point. As writers, we’re taught to use synonyms to avoid repetition and keep things interesting. Humans follow that just fine, but for a model, if you call the same concept five different things, you’re creating what I call “fog.” The model can lose track of what you’re referring to. The solution is to pick one primary term for your core subject and stick with it. These stable labels become “handles” that the AI can grab onto during extraction and compression. Unstable labels just get lost.
As for structured formats, the entire LLM ecosystem is signaling a clear preference. The trend toward structured outputs and constrained decoding tells us that machines want facts delivered in predictable shapes. So, when you embed things like definitions, step-by-step sequences, criteria lists, or comparisons with fixed attributes directly into your prose, you’re essentially pre-packaging your information for them. Your content becomes easier to extract, easier to compress without losing key data, and far easier for an AI to reuse correctly in its own answers. You’re speaking its language.
When an AI misreads content, creators might see their nuanced middle sections turn generic or their brand mentioned without supporting facts. Can you share a real-world example of this happening and walk us through the five-step editing process to fix it and make the content “survive”?
Absolutely. A common symptom I see is a brand getting a mention, but none of its supporting evidence gets carried over into the AI’s answer. The system might say, “Brand X is a solution for this problem,” but it won’t include the specific data or a key differentiator from the middle of the article. This happens because the model couldn’t justify the citation—the proof was too far from the claim. Your brand just becomes background color. Another one is watching your detailed, nuanced breakdown of a complex topic get compressed into a bland, generic summary that completely misses the point.
To fix this, I use a simple, five-step editing workflow you can run in under an hour.
- First, isolate the middle third of your article and read only that. If you can’t summarize it in two sentences without losing the core meaning, it’s too soft and vulnerable.
- Next, add a “re-key” paragraph right at the start of that middle section. Restate the main claim and what’s at stake.
- Then, convert the prose in that section into four to eight distinct “answer blocks.” Each one needs to be quotable on its own, with its own claim and supporting detail.
- After that, do a pass to move proof right next to its claim. If you see a number or source reference paragraphs away from the statement it supports, pull it up.
- Finally, stabilize your labels. Pick the main name for your key concepts and use them consistently throughout the middle.
This process directly addresses both failure modes: the model’s attention bias and the system’s aggressive compression. You’re building a stronger bridge for the information to cross.
What is your forecast for how content strategy will need to evolve as AI systems become the primary intermediaries between information and audiences?
My forecast is that a lot of creators will be fooled by the promise of bigger context windows. They’ll think, “Great, now I can write even longer, more wandering pieces.” But that’s a trap. Longer content just invites more aggressive compression, which makes the “lost in the middle” problem even worse. The most successful content strategists will be those who stop treating the middle of an article like a place to add decorative prose and start treating it like the load-bearing span of a bridge. We have to become architects of information, focusing on a tight, resilient geometry. The future isn’t about writing sterile, machine-readable documentation, but about building content that is so structurally sound it can survive both deep human reading and aggressive machine reuse. The strongest beams must go in the middle.
