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Building a Content Strategy for LLMO

Optimizing content for search engines is no longer enough – now we need to optimize for large language models as well. Enter LLMO: Large Language Model Optimization. LLMO is about adjusting your content strategy so that AI models like ChatGPT, Google’s Gemini, and others can easily digest, trust, and retrieve your information when generating answers. In practice, a content strategy for LLMO looks a lot like good content marketing and SEO, with a few twists to meet the unique needs of AI. In this guide, we lay out how to build and refine your content strategy to improve your brand’s AI visibility.

What is LLMO and Why It Matters

LLMO (pronounced L-L-M-O) is essentially the new layer on top of SEO. Where SEO focused on ranking in search engine results pages, LLMO focuses on being favored by AI-generated results. Why does this matter?

  • AI models are becoming gatekeepers to information. If your content isn’t optimized for them, you might be invisible to a growing segment of users who rely on AI assistants.
  • Traditional SEO signals (backlinks, keywords) still count, but AI also “looks for” qualities like clear context, authoritative tone, and completeness. It “reads” content more like a human than a robot spider.
  • By implementing LLMO best practices, you inherently improve content quality for all users – human readers and AI alike.

Think of LLMO as designing your content so it’s AI-friendly: easy for the AI to interpret correctly and deem worthy of inclusion in answers.

1. Focus on Comprehensive, High-Quality Content

AI models prefer comprehensive answers. When training, they’ve seen which content pieces are referenced and upvoted by humans. They tend to trust content that covers a topic in depth and clearly.

  • Depth over breadth: Rather than many thin posts on every keyword, produce definitive guides or articles on key topics. For instance, instead of 10 short blog posts on various minor questions, create a single in-depth resource that an AI would find useful to pull information from.
  • Address multiple related questions: AI often answers a question by combining bits from different parts of a source. If your content pre-empts those by having a FAQ section or a “common questions” section within an article, you become a one-stop source. For example, a page about “SSD vs HDD storage” might explicitly answer, “Which is faster?”, “Which lasts longer?”, “What do experts recommend?”, etc.
  • Keep it factual and updated: Nothing will get you ignored by an AI faster than outdated or incorrect information. Regularly update your key content with current statistics, latest standards, or new developments. Mark content with last-updated dates (which both users and some crawlers see as a freshness signal).

2. Structure Content for AI Consumption

Structure and formatting help AI parse your content effectively:

  • Use clear headings (H1, H2, H3): Break content into logical sections with descriptive headings. An AI might scan your H2s and H3s to quickly find relevant info to formulate an answer. For instance, in an article on “Email Marketing Best Practices,” have sections like “Improving Open Rates”, “Avoiding Spam Filters”, etc. If someone asks the AI about spam filters, it may zero in on that section.
  • Bulleted or numbered lists: These are gold for both Google snippets and AI answers. If you have “Top 5 tips” as a list, an AI can easily extract that list. In an optimization context, if someone asks “What are the top tips for X?”, the AI might directly include your listed points (perhaps with citation in Google’s case).
  • Tables and data: If applicable, include tables (e.g., feature comparisons, price vs value tables). AI can read and interpret tables; in fact, a concise table may be referenced or even quoted by some AI systems to provide a quick comparison in an answer.
  • Schema markup: Implement structured data like FAQPage schema, HowTo schema, etc. Google’s Gemini will explicitly benefit from this (rich results influence AI summaries), and other AIs indirectly benefit because structured data adds clarity to your content’s meaning. For example, FAQ schema might make your Q&A more likely to be directly pulled in an AI answer box.

3. Align with User Questions (Keyword and Prompt Research)

In traditional SEO, we do keyword research. For LLMO, think in terms of questions and prompts users might use with AI assistants:

  • Research conversational queries: Tools are emerging that show what questions people ask AI (similar to how we see “People Also Ask” in Google). If you can find these, use them. If not, infer from forums, Quora, and your own customer questions. For example, a traditional keyword might be “project management software features,” but a user might ask ChatGPT, “What features should I look for in project management software?”. Your content should be ready to answer that exact question.
  • Incorporate Q&A in your content: Consider having a section or separate posts in a Q&A format. A “Customer Questions Answered” blog series, or simply adding a Q&A part at the end of articles covering likely queries. If your content literally has the question and answer, an AI can easily use that (potentially verbatim).
  • Long-tail and specific scenarios: AI users often ask very specific things (because with AI they can). For instance, instead of “how to improve SEO”, someone might ask “How can a local bakery improve its SEO with a small budget?”. If that’s your target area, weaving such specific scenarios into your content (maybe in case studies or examples) can make the AI more likely to pull your brand or advice when that nuanced question comes.

4. Build Credibility and Trust (E-E-A-T for AI)

Expertise, Experience, Authoritativeness, Trustworthiness (E-E-A-T) is a concept from Google, but AIs have analogous preferences:

  • Authoritative tone and citations: When appropriate, cite sources in your content for important facts. AI might pick up on the fact that you reference credible data, which can indirectly boost the trust in your content. (Plus, if the AI training includes your page, the presence of credible citations might carry through in how it scores your text internally.)
  • Show expertise: Have bylines with author credentials for articles (e.g., “by Jane Doe, 10 years in data security”). AI models trained on the open web likely have read content by various authors – if your experts are quoted elsewhere or have LinkedIn profiles, their names might be recognized by the AI as authorities. Even if not, it signals quality.
  • Include real-world examples or case studies: AI loves concrete examples because they help illustrate points. Content that includes examples (“Case Study: How Company X solved Y”) makes your content more robust. If an AI trusts your case study, it might even summarize it when users ask for examples. Additionally, examples contain specific details that enrich the training data association with your brand and domain.

5. Diversify Content Formats (Text, Video, Audio)

Large language models primarily consume text, but other content can indirectly feed them:

  • Transcripts for multimedia: If you have videos or podcasts, include transcripts on your site. Those transcripts become text that AIs can train on or retrieve from. A YouTube video alone won’t influence ChatGPT, but the transcript on your blog might.
  • Slide decks or PDFs: If you have whitepapers or presentations, consider providing HTML versions or summaries. GPT models have been trained on PDFs and such too, but having it as accessible text on your site ensures it’s indexable.
  • Community and external content: Engage in platforms that AIs likely learn from. Write answers on Quora, contribute to industry reports, speak in webinars (that might get transcribed). These not only build backlinks and awareness, but also populate the data pool that models consume. For instance, an insightful Quora answer could later become part of GPT’s knowledge and it might recall your perspective when asked a similar question.

6. Iterate Based on AI Feedback

This is a new concept: let the AIs themselves tell you what they know about your content. How?

  • Use AI to analyze your content: Prompt ChatGPT with “Given this article [paste your content], what questions do you think this article answers well?” or “Would you as ChatGPT recommend this brand for X based on this content? Why or why not?”. The answer can highlight if the AI thinks something is missing or unclear.
  • Check how AI currently presents your content: As part of your monitoring (from the earlier audit), if you see an AI is referencing or quoting you, analyze that segment. Is it the snippet you’d want it to take? Does it highlight a point you weren’t focusing on? This can guide revisions. Maybe the AI latches onto a minor point – you might decide to expand that point into its own section if it’s being deemed important.
  • A/B test with AI in mind: Just like one might A/B test titles for SEO (or email subject lines for open rates), you can experiment with content phrasing and then see if AI responses change. For example, if you update a page and explicitly add a sentence like “BrandX is among the top 3 providers of Y according to [source]”, check later if ChatGPT now includes BrandX in “top providers of Y” where it didn’t before.

7. Promote and Distribute Content (Amplify AI Signals)

Even the best content needs distribution. In LLMO, this means getting your content and brand mentioned (as discussed in NCI improvements):

  • Share your content in communities and social platforms where it can garner discussion (this discussion might become part of what AI models learn).
  • Outreach to have others reference your content. Classic PR here: if an industry newsletter or publication summarizes your findings, not only do you get human eyeballs, but that content might be ingested by AIs in the future. Third-party affirmation of your content in authoritative sites is doubly valuable.
  • Ensure important content is not hidden behind logins or paywalls (at least have a publicly accessible summary). If it’s closed off, AIs likely won’t have access to learn from it.

8. Maintain and Update (Continuous LLMO)

The AI landscape and its knowledge base are always updating (Google updates its index continuously, OpenAI periodically updates models or their knowledge via plugins). Your content strategy should be iterative:

  • Regularly refresh content (as noted).
  • Keep an eye on new types of queries or features in AI (e.g., if voice AIs start citing sources out loud, maybe having a phonetic-friendly brand name mention matters – speculative, but an example of adapting).
  • Stay informed about AI model changes. If OpenAI announces a new cutoff or Google changes how SGE works, adjust your strategy accordingly. For example, if Google’s AI starts giving 10-point answers instead of 3-point, having content that enumerates more items might become more beneficial.

Conclusion

Building a content strategy for LLMO isn’t about throwing out your SEO playbook – it’s about extending it to meet the needs of AI. By creating high-quality, structured, and authoritative content that aligns with the questions people ask AI, you make it easier for models to pick up your brand in their answers. It’s a win-win: your human audience gets great content, and the AI “audience” finds exactly what it needs to highlight you as an authority.

As AI continues to evolve, so will LLMO tactics. The core principle, however, remains: serve the user’s query in the best way possible. Do that, and both search engines and AI engines will reward you with visibility. In the next post, we’ll shift gears and see which sites are currently excelling in this domain with high NCI scores – giving you real examples to learn from.