The BLUF Strategy: Nail the First 30 Passages of Your Content

AI models have a “F-shaped attention bias” that prioritizes content beginnings. You should utilize the BLUF strategy and place critical information first to win AI citation.

In the previous era of search, the goal of content structure was to keep people scrolling in order to optimize core SEO metrics for the web page. Sometimes, bloggers would even intentionally delay the main point just to boost time on page.

BLUF Strategy: Optimize Content for AI Citations

What is BLUF Strategy?

The BLUF strategy is simply a communication framework in which the most important conclusion or answer appears at the beginning of a document or section. Skip the long-winded intros and background – get straight to what matters most and put your main point, answer, or conclusion right at the top. This way, the user’s core intent is addressed immediately.

This approach isn’t just good for readers who want quick answers; it’s also tailored to how LLMs prefer to “read” information. Observed retrieval and weighting behaviors in LLMs suggest that they tend to pay the most attention to the start of a page and weigh early tokens or sentences more heavily. While LLM attention mechanisms do not literally “ignore” middle content, empirical studies show that earlier passages are more likely to be surfaced during retrieval and summarization, particularly in citation-heavy responses.

The Technical Ceiling: The “First 30 Passages” Constraint

What makes the BLUF strategy even more appropriate in this new era of search of the technical limitation of AI crawlers. For instance, according to an in-depth analysis using Chromium source code and official Google documentation, Chrome’s semantic engine primarily looks at the first 30 passages on a page when it builds its history embeddings.

This limit applies to retrieval and embedding stages, not page rendering, which means that content beyond this threshold is still visible to humans but may be underrepresented in machine recall. If your best ideas are tucked deep in a 2,000-word long-form article, they may fall beyond this technical ceiling and remain invisible to AI’s semantic memory.

In practical terms, writing for AI is a lot like being a news anchor in broadcast journalism: you always open with the headline, because you never know when your audience will tune out or hit a data limit. Put your main conclusion up front, and the AI will actually pick up your expertise before it runs out of “attention.”

How to Structure Your “Atomic Content”

To optimize for both the 200-word passage limit used by algorithms like Chrome’s DocumentChunker and the natural scanning patterns of humans, your content should be divided into self-contained chunks of knowledge, or “atomic” units, so that both people and machines can understand quickly.

• Start with direct answers. Open each article – or even each section – with an explicit, one- or two–sentence summary that immediately addresses the specific question that the heading poses.
• Use proper headings. Mark up your H1, H2, and H3 tags so AI can follow the logic of your page from top to bottom.
• Make things easy to scan. Bullet points, numbered lists, and tables aren’t just for readers – they’re perfect for AI extraction and featured snippets.
• Keep the flow logical. Each chunk should make sense on its own, but your overall document should still move naturally from one idea to the next, so the machine can follow through the page easily.

Why BLUF Strategy Wins for Humans Too

The BLUF strategy isn’t just about pleasing the algorithm; it enhances the reading experience for humans, too.

Nielsen Norman Group research show that most people scan online content in an “F-pattern” – they focus at the start of sections, then fade out halfway through. By putting the answers first, you make your content easier to understand and ready to be cited. In today’s race for attention and AI visibility, the brand that gives the fastest, clearest, and most extractable answers wins. That’s how you build authority and earn a “share of memory” within AI-generated responses.

This emphasis on leading with clear, extractable answers also explains why certain content formats consistently outperform others in AI-generated responses, a pattern we examine in more detail in our article on fan-out queries and how AI systems source information across multiple sub-queries.