The Most Common Beginner Mistakes in LLMO Optimization (and How to Fix Them Fast)

Why LLMO mistakes are so easy to make

LLMO (Large Language Model Optimization) builds on SEO but optimizes content for how large language models (ChatGPT, Gemini, Perplexity and others) read, extract and cite information. Many beginners assume that writing a longer article or adding more keywords is enough. In the AI era, additional signals matter: structure, extractability, data density, schema and EEAT.

1) Treating LLMO as “classic SEO”

The most common misconception is: “We do SEO, so we’re done.” SEO is the foundation, but LLMO determines whether your content can be used as a source inside AI-generated answers.

• Typical problem: the page is optimized for rankings, not for direct answers.
• Impact: AI panels cite competitors even when you rank relatively high.
• Fix: add a “direct answer” section (definition/summary), plus FAQ and clear sub-sections to key pages.

2) Weak structure: huge text blocks with no logic

Models and users prefer scan-friendly content. When long pages lack H2/H3 headings, lists or clear sections, AI struggles to extract answers reliably.

• Typical problem: 3,000 words, but no chapters, lists, or tables.
• Impact: poor extractability and weak “quotability”.
• Fix: use H2 for core sections, H3 for sub-questions; add lists, steps, tables and short definitions.

3) Low data density (too much fluff, too few facts)

AI answers are built on facts, parameters, examples and clear statements. If your text stays generic, there is little that AI can “take” from it.

• Typical problem: “it’s important”, “we recommend”, “it may help” without concrete points.
• Impact: AI prefers pages that contain definitions, checklists, comparisons, and measurable guidance.
• Fix: add definitions, checklists, examples, specific metrics, recommended ranges and step-by-step procedures.

4) Missing FAQ and HowTo sections

FAQ and HowTo formats are extremely reusable for AI. FAQ (question → answer) is especially powerful because models can directly reuse both the question and the answer.

• Typical problem: the article explains a topic but does not answer common user questions.
• Impact: lower chance of citations in AI answers.
• Fix: add 5–10 FAQ questions and one short HowTo section (steps) where it makes sense.

5) Weak EEAT: it’s unclear who the author is and why they’re credible

AI systems and Google prefer sources with clear identity and trust signals. EEAT is not only about the author— it also includes organization visibility, contact details, transparency and reputation.

• Typical problem: no author bio, no “about”, no contact, no policies, no references.
• Impact: content feels anonymous → lower trust and weaker source quality signals.
• Fix: add author details (bio), organization identity, contact info, policies, references and internal links to them.

6) Ignoring schema and JSON-LD (or implementing it poorly)

Schema is not a magic ranking factor, but it dramatically improves machine readability. For LLMO, schemas such as FAQPage, HowTo, Article and Organization help systems interpret the content correctly.

• Typical problem: no JSON-LD at all, or only a basic Article without Organization and without FAQ/HowTo.
• Impact: fewer structured signals for AI, weaker content interpretation.
• Fix: implement at least Article + Organization; add FAQPage when you have FAQ; use HowTo for step-by-step guides.

7) Optimizing content without tracking AI visibility

What you don’t measure, you can’t improve. With LLMO, you should track whether your pages appear as sources in AI answers, and whether AI-ready signals are improving over time.

• Typical problem: “we made changes” without testing and without before/after comparison.
• Impact: random improvements with unclear results.
• Fix: run an LLMO audit before changes and after changes; track structure, JSON-LD, EEAT, extractability and recommendations.

Quick checklist: what to fix first

• Does the page have clear H2/H3 structure and scan-friendly sections?
• Is there a direct definition/summary near the top?
• Does the content include concrete facts, parameters and examples?
• Do you have FAQ (5–10 questions) and, where relevant, HowTo steps?
• Are author and organization signals visible (EEAT)?
• Is JSON-LD complete (Article + Organization + FAQ/HowTo if applicable)?
• Do you measure AI visibility and run audits before/after?

Conclusion

The easiest way to avoid getting stuck in LLMO is to work systematically: first structure and facts, then FAQ/HowTo, then EEAT and schema, and finally measurement and iteration. If you want confidence that you’re prioritizing correctly, use an audit that scores these layers consistently.