The AI Search Content Optimization Checklist [With Google Sheets]

Although there’s a very high overlap in principles to optimizing for AI vs traditional search, there are certainly differences due to the changes in retrieval style (single-query match with pages vs Query fan-out and content synthesis), optimization target (page content and metadata vs content chunks and factual spans), results presentation (Ranked list of clickable links vs Synthesized answer, citations, summaries), and success metrics (Rankings, CTR, traffic vs Inclusion/visibility in response, citations/mentions), among other areas.

To facilitate actionability, I’ve created an AI Search Content Optimization Checklist, going through the most important aspects to take into account to optimize your content for AI search answers -from chunk optimization, citation worthiness, topical breadth and depth, personalization, etc.-, along with their importance and how to take action.

Let’s go through it and get access to the Google Sheets version:

1. Optimize for Chunk-Level Retrieval

AI search engines don’t index or retrieve whole pages — they break content into passages or “chunks” and retrieve the most relevant segments for synthesis. That’s why you should optimize each section like a standalone snippet.

To do: 

• Don’t rely on needing the whole page for context, each chunk should be independently understandable.
• Keep passages semantically tight and self-contained.
• One idea per section: keep each passage tightly focused on a single concept.
• Use structured, accessible, and well-formatted HTML with clear subheadings (H2/H3) for every subtopic.

2. Optimize for Answer Synthesis

AI search engines synthesize multiple chunks from different sources into a coherent response. This means your content must be easy to extract and logically structured to fit into a multi-source answer.

To do: 

• Summarize complex ideas clearly, then expand (A clearly structured “Summary” or “Key takeaways”).
• Start answers with a direct, concise sentence.
• Favor plain, factual, non-promotional tone.
• Use Structured Data to help AI models better classify and extract structured answers.
• Use natural language Q&A format.

3. Optimize for Citation-Worthiness

AI search engines will cite content when it’s perceived as factually accurate, up-to-date, well-structured, and authoritative. Not every included chunk gets cited – to earn attribution, your content must meet higher trust and clarity criteria.

To do: 

• Use specific, up-to-date, verifiable claims, fact-based statements, not vague generalities.
• Include source citations (link to studies, stats, or experts).
• Show authorship and credentials for EEAT signals.
• Use author, organization structured data for brand and entity salience that reinforces citation metadata.
• Refresh key content regularly and signal updated content by adding timestamps.

4. Optimize for Topical Breadth and Depth

Google AI Mode use the query fan-out technique, where a complex query is automatically broken into multiple related subqueries (facets, angles, intents), and those are executed in parallel to retrieve the most relevant content for each aspect, gathering and synthesizing information from diverse sources. This will reward sites with topical breadth and depth, the ones that feature content that covers each facet in-depth. If your site is seen as an authority on the whole topic, multiple subqueries might pull from different pages on your site.

To do: 

• Use a topic cluster model, creating a comprehensive pillar (hub) page for your business, relevant, main broader topics, and cluster pages around specific facets.
• Pillar pages should summarize each topic facet, covering key sections briefly, with links to deeper cluster pages.
• Cluster pages should target specific facets, that should be covered in-depth, ensuring unique purpose and query intent for each page.
• Cross-link between cluster pages where relevant and back to the hub, as a central resource, establishing semantic relationships across content, and helping AI understand the full context and span connections between topics.
• Query fan-out explores different user intents, so targeting a diversity of angles increases coverage.

5. Optimize for Multi-Modal Support

AI search systems are increasingly retrieving and synthesizing multimodal content, -including images, charts, tables, videos-, to better answer user queries, giving opportunity to provide more useful, scannable and engaging answers for users.

To do: 

• Ensure images and videos crawlability for search and AI bots.
• Serve images via clean HTML and avoid lazy-loading with JS-only rendering, since LLM-based scrapers may not render JS-heavy elements.
• Images should use descriptive alt text that includes topic context.
• Add captions to images and videos with explanation right below or beside the visual.
• Use <figure>, <table>, etc. with contextually correct markup to help parse tables, figures, lists.
• Avoid images of tables, use HTML tables instead for a machine-readable format supporting tokenization and summarization.

6. Optimize for Content Authoritativeness Signals

Authority increases the likelihood that your content will be included and cited in AI-generated answers, especially as these systems rely on entity recognition and reputation to determine which sources to trust. Without clear authority signals -such as expert bylines, structured data, external citations, and mentions on reputable sites- your content is less likely to be surfaced, even if it’s accurate.

To do: 

• Optimize your brand presence across web platforms, including social channels, in a consistent way, linking back to your main site, engaging with your community, answering reviews, etc.
• Publish original research, reports, or data studies, conduct surveys, compile unique datasets, or run industry studies. Promote them to journalists and bloggers who create content roundups.
• Secure coverage in industry and expert publications, contribute quotes or guest content to respected newsletters and blogs in your industry.
• Promote your content across relevant third-party channels: Engage with influencers, experts, Slack groups, subreddits, and communities. Ask for feedback and mentions.

7. Optimize for Personalization Resilient Content

AI search engines can personalize answers using a combination of contextual signals, retrieval techniques, and user-centric data: User location, user intent, search history or session context, entity familiarity or brand bias via user patterns, user feedback and engagement.

To do: 

• Cover multiple intents for the same topic, so your content aligns with many personalized subqueries, increasing surface area.
• Optimize for localized intent by including regional content, currencies, addresses, or local schema markup (Place, LocalBusiness).
• Add contextual signals that aligns content with profile-based personalization, segmenting content for specific personas or use cases.
• Get links and mentions across reputable domains and popular platforms where your audience engages via digital PR, contributor posts, Wikipedia citations, mentions in research, strong community and social media presence and engagement, since AI search may personalized towards brands or sites the user previously interacted with or that have high entity recognition for a given topic.
• Retain attention and engagement with fast, useful content that gives a satisfying user experience since AI search systems refine results based on user behavior, thumbs up/down, etc. This feedback loops into ranking and synthesis decisions for future answers.

8. Optimize for content crawlability and indexability

Relevant content must be accessible, indexable, and reusable by both traditional search engine crawlers and AI-specific agents that retrieve content for large language models and AI-generated answers.

To do: 

• Allow search engine crawlers that deed AI systems through your robots.txt directives in the areas featuring relevant content to surface: GPTBot, Googlebot and Google-Extended token, bingbot, Claude (ClaudeBot/Claude-User/Claude-SearchBot), CCBot, PerplexityBot/Perplexity‑User.
• Avoid blocking AI bots with firewalls or bot filters, by whitelisting their IP ranges.
• Render all essential content server-side or use pre-rendering. Avoid Client Side Rendered JavaScript reliance for key content to avoid indexability challenges as not all AI systems render it.
• Avoid noindexing via meta robots valuable content to be surfaced in AI answers.
• Avoid using a “nosnippet” rule via meta robots in valuable content to be surfaced in AI answers, which will prevent the content from being used as a direct input for AI Overviews and AI Mode.
• Use canonical tags to specify content to be retrieved and used in synthesis from the right pages URLs versions.
• Optimize internal linking to facilitate internal pages crawlability, while using descriptive anchor texts.

Top sources and more information about AI search content optimization:

• Engineering Relevant Content: Tips to Get Your Content into LLMs by Francine Monahan
• How AI Mode Works and How SEO Can Prepare for the Future of Search by Mike King
• Chunked, Retrieved, Synthesized – Not Crawled, Indexed, Ranked by Duane Forrester
• Writing and optimizing content for NLP-Driven SEO by Jan-Willem Bobbink
• How Content Structure Matters for AI Search by Chris Green
• Revisiting ‘useful content’ in the age of AI-dominated search by Amanda King
• Query Fan-Out: A Data-Driven Approach to AI Search Visibility by Andrea Volpini
• What does Google’s AI Mode really want from your product page — and what exactly is Chunk Optimization? by Andrea Volpini

Find these and more resources in the Optimizing Content for AI Search Section in LearningSEO.io. 

Get the AI Search Content Optimization Checklist in Google Sheets

Start using the AI Search Content Optimization Checklist by copying it from Google Sheets here.