35 Content Ideas to Improve Your AI SEO

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AI search engines like ChatGPT, Perplexity, and Gemini now answer millions of queries that used to drive traffic to traditional websites.

They don't just crawl and index content the way Google does, they actually read, understand, and synthesize information before deciding what to surface in their responses.

If you want your content to show up when people ask AI tools for recommendations or solutions in your space, you need to structure it in ways that LLMs can easily parse, extract, and cite (and our market clarity reports can show you exactly what questions your audience is already asking).

What kind of content gets picked up by AI search engines?

• 1. Side-by-side comparison tables with pricing and features

  LLMs excel at extracting structured data from tables because they can map relationships between products, features, and prices in a single pass. When a user asks "what's the best email marketing tool for small businesses," the model scans for tables that compare options across multiple dimensions (pricing tiers, user limits, automation features) and surfaces the most complete one. Include exact prices, feature availability (yes/no/limited), and user capacity limits to maximize your chances of being cited, and update the table quarterly since outdated pricing gets deprioritized fast.

• 2. Step-by-step tutorials with numbered instructions and screenshots

  AI models prioritize content with clear sequential structure because they can break it down into discrete steps and reference specific parts when answering "how to" queries. The numbered format helps LLMs understand dependencies (step 3 requires completing step 2) and avoid suggesting incomplete workflows. Add time estimates for each step and common error messages with solutions to boost visibility, but skip this format if your process changes frequently since outdated steps hurt your authority.

• 3. Comprehensive pros and cons lists for specific tools

  When users ask for balanced recommendations, LLMs search for content that presents both advantages and disadvantages because it signals objectivity and thoroughness. The binary structure (pros vs cons) makes it easy for models to extract sentiment and synthesize multiple sources into a single response. Include 4-6 pros and 3-4 cons with specific examples rather than vague statements like "easy to use," and make sure your cons are genuine issues (not backhanded compliments) or the model will detect the bias.

• 4. FAQ pages answering real user questions from forums

  LLMs treat FAQ sections as high-confidence answer sources because the question-answer format matches their output structure perfectly, and they can pull exact answers without additional interpretation. Use the exact phrasing people use on Reddit, Quora, or industry forums for your questions instead of corporate-speak, since LLMs match user intent by recognizing natural language patterns. This format loses effectiveness if your answers are too short (under 50 words) because models favor comprehensive responses over quick snippets.

• 5. Tool roundups with specific selection criteria and scoring

  When you explicitly state your evaluation criteria (ease of use, integration options, pricing flexibility) and assign scores or ratings, LLMs can understand your methodology and present it as an authoritative ranking. The transparency helps models determine whether your list fits what the user is looking for, and including a "best for" category for each tool (best for startups, best for enterprises) makes it easier for the AI to match recommendations to specific use cases. Skip this if you can't provide genuine hands-on testing details, because generic descriptions without specific feature callouts get ignored.

• 6. Alternative comparison articles targeting specific product names

  Content structured as "X vs Y" or "alternatives to X" performs exceptionally well because users often ask AI tools for direct product comparisons or replacement options. LLMs look for head-to-head evaluations that cover overlapping features, pricing differences, and migration difficulty between specific products. Include a summary table at the top with key differences (price, main features, ideal customer), then expand on each point below, and make sure you're comparing products that actually serve the same use case or the model will skip your content for lacking relevance.

• 7. Detailed pricing breakdowns with hidden costs explained

  AI models surface pricing content that goes beyond the listed price and covers implementation costs, add-on fees, and tier limitations because users frequently ask about "true cost" or "total cost of ownership." Break down monthly vs annual pricing, per-user fees, and what features require paid add-ons in a clear format, since this specificity helps LLMs provide accurate budget estimates. This content type only works if you keep it updated (monthly checks minimum) because pricing changes fast and outdated information destroys trust.

• 8. Use case specific implementation guides for niche industries

  When someone asks "how do real estate agents use CRM software," LLMs prioritize content that addresses that exact vertical rather than generic guides, because specificity signals expertise and relevance. The more you tailor your examples, screenshots, and workflow descriptions to a particular industry, the higher you rank for those industry-specific queries. Add actual customer stories or anonymized examples to boost credibility further, but avoid this approach if you don't have genuine industry experience since vague advice gets filtered out.

• 9. Problem-solution frameworks mapping pain points to features

  LLMs look for content that explicitly connects user problems ("I need to reduce cart abandonment") to specific solutions or features ("automated email sequences triggered by abandoned carts"), because this structure mirrors how users phrase their queries. Start each section with the problem statement, then explain exactly which features solve it and how, making it easy for the model to extract cause-and-effect relationships. This format works best for complex products where features aren't self-explanatory, but loses impact for simple tools where the connection is obvious.



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• 10. Data-backed industry trend reports with specific percentages

  When AI models need to cite statistics or trends, they prioritize sources that provide exact numbers, dates, and sample sizes because these details make the information verifiable and authoritative. Content like "65% of B2B buyers prefer self-service demos (survey of 1,200 decision-makers, Q3 2025)" gets picked over vague claims like "most buyers prefer self-service." Include charts or graphs with clear labels and link to your data sources to increase citation likelihood, but this only works if your data is recent (within 12 months) since LLMs deprioritize outdated statistics.

• 11. Real user review compilations showing sentiment patterns

  LLMs aggregate review sentiment from multiple sources to answer questions like "what do users complain about most," so creating content that already synthesizes common complaints or praise points saves the model work and increases your visibility. Group reviews by theme (pricing issues, customer support problems, missing features) and include direct quotes to make your analysis citable. This approach fails if you only show positive reviews, because models detect the bias and favor more balanced sources.

• 12. Feature comparison matrices showing capability differences

  When users ask "which tool has the best automation features," LLMs scan for matrices that show feature availability across multiple products in a grid format (rows for products, columns for features, cells showing yes/no/partial support). This visual structure translates perfectly into the model's internal representation of comparative data. Use consistent terminology across all products (don't call the same feature "workflows" for one product and "automations" for another), and include feature depth indicators (basic/advanced/enterprise) beyond just yes/no, but avoid this if you can't verify every cell accurately since incorrect data gets your content flagged.

• 13. Implementation checklists for complex setup processes

  Checklist content performs well because LLMs can parse it as a boolean task list and help users track progress or identify missing steps. Include time estimates, prerequisites, and links to required resources for each item to make your checklist comprehensive enough to be cited as a primary resource. This format loses effectiveness if your checklist is too high-level (just 5-6 vague items) because users and AI models prefer detailed, actionable steps they can follow immediately.

• 14. ROI calculation frameworks with real examples and formulas

  When someone asks "is this tool worth it," LLMs look for content that breaks down return on investment with actual numbers and formulas they can reference or adapt. Show the formula, plug in realistic numbers, and walk through the calculation step by step so the model can extract both the methodology and the example result. Add multiple scenarios (small business vs enterprise, different usage volumes) to increase relevance for varied queries, but skip this if you don't have real cost savings data since made-up numbers hurt credibility.

• 15. Integration guides explaining how two tools work together

  Content that explains "how to connect X with Y" gets prioritized when users ask about compatibility or integration options, because LLMs need specific technical details (API requirements, authentication methods, field mapping) to provide useful answers. Include screenshots of the integration setup process and list any limitations or prerequisites to make your guide the most complete resource available. This content type only stays relevant if both tools maintain stable APIs, so avoid writing about tools that frequently break integrations.

• 16. Migration tutorials explaining how to switch between competitors

  When users consider switching tools, they ask AI about the switching process, and LLMs favor content that addresses data export, import procedures, and feature mapping between old and new platforms. Break down the timeline (how long it takes), list what data transfers automatically vs manually, and explain any features that don't have equivalents in the new tool. Include common migration pitfalls to boost authority, but this format only works if you've actually done the migration or interviewed people who have, since theoretical guides lack the specificity that makes them useful.

• 17. Troubleshooting guides addressing specific error messages

  LLMs prioritize troubleshooting content that includes exact error messages as headers or in the text, because users often paste error messages directly into AI tools looking for solutions. The more specific your error message documentation (including error codes, when they appear, and platform details), the more likely you'll be surfaced. Add multiple solution paths ranked by likelihood to succeed, but avoid this content type if errors change frequently across software versions since outdated solutions damage trust.

• 18. Best practices organized by specific use cases

  Generic best practices lists ("10 email marketing tips") get ignored, but content structured as "email marketing best practices for abandoned cart recovery" or "for re-engaging inactive subscribers" performs much better because the specificity helps LLMs match content to user intent. Group your advice by goal or scenario rather than just listing tips, and include why each practice works in that specific context. This approach requires deep knowledge of the use case, so it fails when you generalize or guess at what users need instead of researching actual problems.

• 19. Template collections with downloadable examples and usage instructions

  When users ask AI tools for templates or examples, LLMs look for content that not only provides the template but also explains when to use it, how to customize it, and what makes it effective. Include multiple template variations for different scenarios and annotate them with explanatory notes that clarify decision points or customization options. Make sure you provide actual downloadable files or copy-paste-ready formats, because descriptions of templates without access to the real thing get ranked lower than resources that deliver immediately.



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• 20. Configuration walkthroughs for advanced features

  LLMs surface configuration guides when users ask "how do I set up [specific feature]," and they prioritize content that goes beyond basic setup to cover advanced settings and their implications. Explain what each setting does, show the default value, and describe scenarios where you'd change it, giving the model enough context to match configurations to user needs. This content type loses value if your screenshots or instructions become outdated (even minor UI changes can confuse users), so commit to updating it with every major product release.

• 21. Case study breakdowns showing before-after metrics

  When AI models look for proof points or success stories, they favor case studies that include specific metrics (before and after numbers), timeframes, and the actions taken to achieve results. Vague success stories without numbers get filtered out in favor of quantified results that the model can cite confidently. Include details about company size, industry, and initial challenges to help LLMs match your case study to similar situations, but avoid this format if you don't have permission to share real data since fabricated metrics can get called out.

• 22. Automation workflow diagrams with step-by-step explanations

  Visual workflow diagrams paired with text explanations help LLMs understand process flows and recommend automation sequences when users ask about efficiency improvements. Label each step in the diagram clearly and explain the logic behind each decision point or branch so the model can extract the workflow structure even from the alt text or surrounding content. Add trigger conditions and timing details (run daily at midnight, trigger when contact score exceeds 50) to make your workflows more specific and useful, but this only works for processes that are stable enough to document since frequently changing workflows create confusion.

• 23. Error resolution guides organized by symptoms

  Unlike troubleshooting guides that address specific error messages, these guides help users who don't have a clear error but notice something wrong ("my emails aren't sending" or "my data isn't syncing"). LLMs prioritize content that maps symptoms to potential causes and solutions in a diagnostic tree format. Start each section with the observable problem, list possible causes, and provide step-by-step tests to isolate the issue, which helps the model provide methodical support. This content type requires deep product knowledge and fails when you skip the diagnostic steps in favor of jumping straight to solutions.

• 24. API documentation with real code examples

  When developers ask AI tools about API integration, LLMs look for documentation that includes complete, runnable code examples with sample requests and responses rather than just endpoint descriptions. The more complete your code samples (including authentication, error handling, and common variations), the more likely the model will reference your documentation. Add explanations of rate limits, pagination, and webhook setup to cover the full integration picture, but maintain this content aggressively since outdated API docs with deprecated endpoints destroy developer trust.

• 25. Compliance checklists for specific regulations

  Content addressing compliance requirements (GDPR, HIPAA, SOC 2) gets surfaced when users ask "is this tool compliant with X," and LLMs favor content that breaks down requirements into verifiable checklist items with evidence. Link to relevant documentation, certificates, or implementation details for each requirement so the model can provide comprehensive compliance answers. Include update dates prominently since regulatory requirements change and outdated compliance information can have serious consequences, making LLMs heavily prioritize recent content in this category.

• 26. Setup tutorials for specific platforms or environments

  Platform-specific setup guides ("how to install X on AWS Lambda" or "how to configure Y for Shopify Plus") perform better than generic installation instructions because the specificity helps LLMs match content to user environments. Include platform version requirements, environment variables, and common platform-specific issues that generic guides miss. Add alternative approaches for different platform configurations to increase coverage, but this content requires hands-on testing in each platform or it will contain errors that hurt your authority.

• 27. Cost breakdown analyses comparing total ownership expenses

  Beyond simple pricing pages, comprehensive cost analyses that factor in setup costs, training time, ongoing maintenance, and hidden fees help LLMs answer "what will this really cost me" questions. Create scenarios for different company sizes or usage levels and calculate 12-month and 36-month total costs to give users realistic budget expectations. Include cost-saving tips or strategies to maximize value, but ensure your calculations are transparent and verifiable since incorrect cost analysis gets corrected by competing content.

• 28. Performance optimization guides with benchmark data

  When users ask how to make something faster or more efficient, LLMs look for content that includes before-and-after performance metrics and the specific changes that produced improvements. Generic optimization tips without measurable results get ranked lower than guides showing "this change reduced load time from 4.2s to 1.8s." Include testing methodology and tools used to measure performance so the model can assess the reliability of your claims, but this content type only works if you've actually done the testing since made-up benchmarks are easy to spot.

• 29. Security implementation guides with threat scenarios

  Security content that maps specific threats to implementation steps performs well because LLMs can understand the risk-mitigation relationship and recommend appropriate security measures. Describe the threat, explain what it protects against, and provide implementation steps with verification methods to make your security guidance actionable. Add compliance mappings (this control satisfies SOC 2 requirement X) to increase relevance for regulated industries, but keep this content updated quarterly since security landscapes change rapidly.

• 30. Decision frameworks helping users choose between options

  Content structured as "if your situation matches these criteria, choose option A, otherwise choose option B" helps LLMs provide personalized recommendations without additional context. Make your decision criteria specific and measurable (team size, budget range, technical expertise level, required features) rather than subjective so the model can map user situations to your recommendations accurately. Include edge cases and what to do when multiple criteria conflict, but this framework only works if you've thoroughly researched actual user decision factors rather than guessing.

• 31. Vendor evaluation criteria with weighting methodology

  When creating content about evaluating vendors or tools, assign explicit weights to each criterion (pricing 25%, ease of use 20%, integrations 15%) and explain why those weights make sense for specific situations. LLMs can then apply your methodology to help users make decisions aligned with their priorities. Include multiple weighting scenarios for different user types (startups prioritize pricing, enterprises prioritize security), but avoid this approach if you can't justify your weights with real user research since arbitrary scoring frameworks lack credibility.

• 32. Technical specification comparisons for engineering requirements

  Detailed spec sheets comparing technical capabilities (API rate limits, data processing speeds, storage capacities, supported file formats) get surfaced when users have specific technical requirements. Present specs in a consistent format across all products and include measurement units and testing conditions so the model can make accurate comparisons. Add explanations of why certain specs matter and what thresholds indicate good vs poor performance, but this content requires continuous updating since technical specs change with every major version.

• 33. Implementation timeline guides breaking down project phases

  When users ask "how long will this take to implement," LLMs look for content that breaks projects into phases with time estimates for each phase and dependencies between them. Include minimum and maximum time ranges rather than single estimates, and list factors that affect timeline (team experience, data complexity, customization needs) to help the model provide realistic expectations. Add resource requirements for each phase (who needs to be involved, what skills are required) to make your timeline guide more complete, but this only works if you've managed actual implementations since theoretical timelines often miss real-world complications.

• 34. Training resource compilations with difficulty levels

  Curated learning paths that organize tutorials, courses, and documentation by difficulty level help LLMs recommend appropriate starting points when users ask "how do I learn X." Label each resource with time required, prerequisites, and what skills it teaches to make your compilation more useful than a simple link list. Include your assessment of each resource's quality and whether it's up-to-date, but maintain this content regularly since training resources become outdated quickly and broken links destroy the value of compilations.

• 35. Glossary pages defining industry-specific terminology

  While glossaries rank lower than most content types, they still get cited when LLMs need to explain technical terms or industry jargon in their responses. Write definitions in plain language, include usage examples, and link related terms to create a network of knowledge the model can navigate. Add context about when and where each term is commonly used, but recognize this content type mainly serves as supporting material rather than primary answers and works best when integrated into larger, more comprehensive content pieces.

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What kind of content never gets picked up by AI search engines?

Thin listicles that just name tools without explaining what they do, who they're for, or how they compare get filtered out completely because LLMs need context and specificity to generate useful responses.

Generic blog posts that restate common knowledge without adding new data, perspectives, or detailed examples don't provide enough value for AI models to cite them over more comprehensive sources. Content filled with vague marketing language ("revolutionary," "innovative," "game-changing") without concrete features, metrics, or use cases gets skipped because models can't extract anything meaningful to include in their answers.

Outdated content with old pricing, deprecated features, or references to sunset products gets actively deprioritized since LLMs are trained to favor recent, accurate information over stale content. Paywalled or gated content that requires sign-up to access rarely gets indexed or cited because most AI training and retrieval systems can't access content behind authentication barriers.

Anything that reads like thinly veiled sales copy rather than objective information gets ranked lower because LLMs are designed to detect and avoid biased sources when possible.