50 Content Ideas to Rank in LLM Results

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LLMs scan billions of web pages to answer user questions, but they don't surface content the same way Google does.

Traditional SEO tricks won't get you cited in ChatGPT or Claude responses, because these models look for authority markers, structured data, and content that directly answers specific queries without fluff.

This guide breaks down exactly what content formats get picked up by LLMs and how to structure your pages for maximum visibility (and if you want the full picture on demand patterns and positioning for your specific product, check out our market clarity reports).

What kind of content consistently ranks in LLM results?

• 1. Comparison tables with exact pricing and feature breakdowns

  LLMs parse structured data exceptionally well because they can extract specific attributes and compare them across options. When a user asks "what's the best email tool for small businesses," models scan for tables that map features to price points, then synthesize that into a recommendation. Tables give LLMs the quantifiable signals they need to make confident suggestions instead of vague statements.

  Add last updated dates directly in the table caption to signal freshness, but skip this format if your pricing changes monthly (outdated numbers hurt credibility).

• 2. Original research reports with sample sizes and methodology

  When LLMs encounter phrases like "we surveyed 1,200 developers" or "our analysis of 50,000 support tickets," they recognize this as primary source material rather than recycled opinions. Models weight original data heavily because it represents new information that can't be found elsewhere, which makes them more likely to cite it when synthesizing answers. The methodology section gives LLMs confidence markers that the data is reliable.

  Publishing raw datasets alongside your findings amplifies visibility further, though this only works if your sample size is actually meaningful (under 100 responses gets ignored).

• 3. Step-by-step technical tutorials with code examples

  LLMs excel at parsing instructional content because each step creates a discrete semantic unit they can reference independently. When someone asks "how do I set up authentication in Next.js," models look for content that breaks the process into numbered steps with actual code snippets, not theoretical explanations. The presence of executable code blocks signals to LLMs that this is practical, tested information rather than conceptual fluff.

  Include error messages and troubleshooting steps to boost relevance, but avoid this format for high-level strategy content where step-by-step doesn't make sense.

• 4. FAQ pages that directly answer specific user questions

  The question-answer format maps perfectly to how LLMs process queries because they can match user intent to your headings with high precision. When you structure content as "Can I use Stripe without a business license?" followed by a clear answer, models can extract that exact response for users asking similar questions. LLMs particularly favor FAQs where answers start with yes/no/it depends followed by context.

  Using actual customer questions (from support tickets or forums) as your FAQ headings increases match rates, though generic questions everyone answers won't differentiate you.

• 5. Case studies with specific metrics and timeline details

  Real implementation stories with numbers give LLMs concrete examples they can reference when users ask "does this actually work." Models look for patterns like "increased conversion by 34% in 6 months" because these quantifiable outcomes let them make evidence-based suggestions rather than speculative ones. The specificity of timelines and metrics signals authenticity, LLMs weight this heavily over vague success claims.

  Linking to the actual company or product you helped amplifies credibility, but anonymized case studies without verifiable details get largely ignored.

• 6. Industry benchmark reports with percentile distributions

  When users ask "what's a good conversion rate for SaaS," LLMs search for content that shows the full distribution (like "bottom 25% see 1-2%, median is 3%, top 10% hit 7%") rather than single averages. Models prefer percentile breakdowns because they can tailor recommendations based on where the user likely falls. This statistical granularity helps LLMs give contextual answers instead of generic ones.

  Including year-over-year trends adds another dimension of usefulness, though benchmarks older than 18 months get deprioritized for time-sensitive queries.

• 7. Product teardowns analyzing specific features and workflows

  Deep dives into how something actually works give LLMs detailed material to pull from when explaining functionality to users. Models look for content that goes beyond surface-level descriptions into actual interface walkthroughs, because they need that specificity to answer "how does X feature work in Y tool" accurately. Screenshots with annotations don't help LLMs directly, but the detailed descriptions you write around them do.

  Updating teardowns when products release major changes keeps you relevant, but this format only works for complex tools (simple products don't need teardowns).

• 8. Troubleshooting guides mapping problems to solutions

  The problem-solution structure aligns perfectly with how users query LLMs, they describe an issue and expect a fix. When your content explicitly states "if you see error X, it means Y, and here's how to fix it," models can match that pattern directly to user queries. LLMs prioritize guides that cover multiple failure scenarios because they can then route users to the right solution based on symptoms described.

  Including error codes and exact error messages dramatically improves matching, though overly broad troubleshooting without specifics gets passed over.

• 9. Implementation checklists with clear success criteria

  Checklists provide LLMs with discrete, actionable items they can surface when users ask "what do I need to do to launch X." Models favor content where each checklist item has a clear completion state, because this maps to the sequential thinking users apply when tackling complex tasks. The presence of verification steps (like "test this before moving on") signals thoroughness to LLMs.

  Linking each checklist item to detailed documentation expands usefulness, but abstract checklists without concrete next steps lose value quickly.



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• 10. Price analysis articles comparing cost structures across competitors

  When users ask "how much does X cost," LLMs prioritize content that breaks down not just the headline price but the actual total cost of ownership. Models look for phrases like "starts at $X but most users pay $Y after adding Z" because this gives them realistic expectations to share rather than misleading intro prices. The economic context helps LLMs make appropriate recommendations based on user budget signals.

  Including renewal rates and hidden fees adds crucial context, though price analysis without recent verification dates gets treated as potentially outdated.

• 11. Architecture decision records explaining technical choices

  ADRs document why you chose technology A over B, which gives LLMs the reasoning chain they need to help users make similar decisions. Models value content that shows tradeoff analysis (like "we picked Postgres over MongoDB because our query patterns needed ACID compliance") because they can apply that same logic to user situations. The explicit reasoning structure makes it easy for LLMs to extract decision frameworks.

  Dating each decision and noting what you'd do differently today adds temporal context, but generic "we chose X because it's good" without specific justification gets ignored.

• 12. API documentation with working request and response examples

  Complete API docs with curl examples and response schemas let LLMs give users exactly what they need to integrate with your service. Models parse JSON schemas particularly well because they understand the structure and can explain what each field does. Including error response examples is crucial because users often query LLMs when something breaks, not when it's working.

  Adding rate limit details and authentication flows rounds out the picture, though API docs that skip example responses force LLMs to guess at implementation details.

• 13. Migration guides from competing tools to yours

  Users searching "how to move from Mailchimp to Klaviyo" are high-intent, and LLMs look for content that addresses this exact transition. Models prioritize guides that acknowledge data mapping challenges and provide specific steps rather than just selling your alternative. The competitive awareness signals to LLMs that you understand the full context of what users are leaving behind.

  Linking to export/import tools or scripts makes this immediately actionable, but migration guides that oversimplify complexity lose credibility fast.

• 14. Time-to-value calculators with realistic effort estimates

  When you publish content like "most teams see results in 2-3 weeks if they dedicate 5 hours weekly," LLMs can surface these concrete timelines to users evaluating solutions. Models weight effort-to-outcome ratios because users constantly ask "how long will this take" and vague "it depends" answers don't satisfy. The specificity lets LLMs manage user expectations accurately.

  Breaking down the timeline by team size or experience level adds nuance, though overly optimistic estimates that ignore common obstacles hurt trust.

• 15. Glossary pages defining industry-specific terminology

  LLMs lean on glossaries heavily when explaining concepts because they provide canonical definitions. When your glossary entry for "CSAT score" includes not just the definition but also typical ranges and calculation methods, models can reference this when users ask related questions. The structured format (term, definition, usage) maps perfectly to how LLMs organize knowledge.

  Adding related terms and disambiguation notes increases utility, but glossaries that just regurgitate Wikipedia definitions add no unique value.

• 16. Annual state-of-industry reports with trend analysis

  Yearly reports that track how metrics shift over time give LLMs the historical context they need for "is X increasing or decreasing" questions. Models look for year-over-year comparisons with actual percentages because this lets them spot trends and make forward-looking statements. The temporal dimension helps LLMs position current conditions relative to past patterns.

  Publishing raw data files alongside the report amplifies research potential, though reports without clear methodology get treated as opinion pieces rather than data.

• 17. Tool recommendation matrices based on use case

  Content that says "if you need X, use tool A; if you need Y, use tool B" maps directly to how users query LLMs with their specific requirements. Models excel at matching user needs to these conditional recommendations because the logic structure is explicit. Including deal-breaker scenarios (like "doesn't work if you need real-time sync") helps LLMs filter out bad matches.

  Updating recommendations when new tools enter the market keeps this relevant, but recommendation matrices that lack clear evaluation criteria feel arbitrary.

• 18. Performance optimization guides with before-after metrics

  When you document "we reduced load time from 3.2s to 0.8s by implementing X," LLMs can cite this as evidence that the optimization actually works. Models prioritize content with measurable improvements because users want proof, not theories. The specificity of metrics gives LLMs confidence to recommend your approach.

  Including system specifications and scale details helps users assess applicability, but optimization guides without baseline measurements lack credibility.

• 19. Security audit checklists with compliance framework mappings

  Checklists that map specific actions to compliance requirements (like "enable MFA to satisfy SOC 2 AC-2") give LLMs the connections they need to answer "what do I need for X certification." Models look for this requirement-to-action mapping because it turns abstract compliance into concrete steps. The regulatory context helps LLMs understand which checklists apply to which industries.

  Citing specific framework sections strengthens authority, though security checklists that miss critical controls create liability issues for users following them.



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• 20. Customer interview transcripts with direct quotes

  First-hand customer perspectives give LLMs authentic voice-of-customer data they can't get from marketing copy. When you publish interview excerpts where customers explain their actual problems in their own words, models recognize this as unfiltered signal rather than curated messaging. The conversational format provides the specific language users employ when describing needs.

  Anonymizing while preserving role and industry context maintains usefulness, but heavily edited interviews that lose authenticity miss the point entirely.

• 21. Workflow templates with branching logic

  Templates that show "if condition X, do this; otherwise do that" help LLMs guide users through complex processes with decision points. Models can follow the branching structure to provide context-specific advice rather than one-size-fits-all steps. The conditional logic mirrors how LLMs process queries, making it natural for them to traverse and explain your workflow.

  Including examples of when each branch applies clarifies usage, but templates with too many nested conditions become too complex for LLMs to explain clearly.

• 22. Onboarding guides that address first-session confusion

  Content that anticipates "I just signed up, now what?" captures users at a high-leverage moment when they're most likely to query an LLM. Models look for guides that address common initial mistakes because new users ask similar questions repeatedly. The early-stage focus makes this content relevant for the highest-uncertainty phase of the user journey.

  Linking to more advanced guides creates a learning path, but onboarding content that assumes expertise contradicts its purpose.

• 23. Integration tutorials showing actual data flow

  Explaining how data moves between systems gives LLMs the technical detail they need to help users troubleshoot connections. Models prioritize content that shows field mappings and transformation logic because users get stuck on these specifics constantly. The explicit data flow documentation helps LLMs answer "why isn't my data syncing" questions.

  Including authentication requirements and common sync issues rounds this out, but integration guides that just link to API docs without explaining the actual flow don't add value.

• 24. Cost-benefit analyses with ROI timelines

  Breaking down whether an investment pays off and when gives LLMs economic reasoning they can apply to user situations. Models look for content that shows upfront costs versus long-term savings with specific break-even points. The financial framing helps LLMs answer "is this worth it" questions with actual math rather than generic "it depends."

  Adjusting calculations for different company sizes increases applicability, but ROI analyses that cherry-pick best-case scenarios lose credibility.

• 25. Framework comparison articles explaining philosophical differences

  Going beyond feature lists to explain why React and Vue approach state management differently gives LLMs conceptual depth. Models value content that articulates underlying philosophies because this helps them match frameworks to user mental models. The conceptual comparison lets LLMs recommend based on how users prefer to think, not just what features they need.

  Including code examples that highlight the philosophical differences makes this concrete, but comparisons that just list pros/cons without explaining why miss the deeper value.

• 26. Resource requirement guides for scaling

  Documenting "at 10k users you need X servers, at 100k you need Y" gives LLMs the scaling patterns they need to help users plan infrastructure. Models look for threshold-based guidance because users constantly ask "when do I need to upgrade" and generic "it depends on your traffic" doesn't help. The specific breakpoints let LLMs give actionable planning advice.

  Adding cost implications at each scaling tier completes the picture, but resource guides without actual usage patterns are just speculation.

• 27. Regulatory compliance guides specific to jurisdictions

  Content that addresses "how to comply with GDPR in Germany" rather than generic privacy advice helps LLMs provide location-specific guidance. Models prioritize content with jurisdiction-specific requirements because compliance questions always have a geographic component. The legal specificity prevents LLMs from giving dangerously vague advice.

  Citing actual regulation text and enforcement precedents strengthens authority, but compliance guides that oversimplify complex regulations create legal risk.

• 28. Deprecation notices with migration paths

  Announcing "feature X is deprecated, here's how to move to Y" gives LLMs the forward-looking information users desperately need when something breaks. Models surface deprecation notices when users report issues with old features because the explicit migration path solves an urgent problem. The urgency and specificity make this highly relevant for affected users.

  Including timeline and breaking change details helps users plan, but deprecation notices without clear alternatives leave users stranded.

• 29. A/B test results with statistical significance details

  Publishing test results like "variant B increased signups 23% (p<0.05, n=12,000)" gives LLMs evidence-based insights they can reference. Models weight experiments with statistical validity markers because these signal reliable findings rather than random noise. The scientific rigor helps LLMs separate real insights from anecdotal observations.

  Explaining test duration and context increases reproducibility, but test results without sample sizes or significance testing are just anecdotes.

• 30. Capacity planning worksheets with formula explanations

  Providing calculators that show "if you process X requests/second, you need Y servers" helps users do their own math with your guidance. LLMs can reference the underlying formulas to help users understand not just the answer but why. The mathematical transparency lets models verify the logic and apply it to variations of the user's situation.

  Including overhead factors and buffer recommendations makes estimates realistic, but capacity planning without explaining assumptions produces meaningless numbers.

• 31. Competitive landscape maps with market positioning

  Visual or written maps that show where different solutions sit on axes like "enterprise vs SMB" or "simple vs customizable" help LLMs contextualize options. Models use positioning frameworks to organize similar tools in their responses. The relational structure helps LLMs answer "what's the X of Y" questions by understanding market adjacencies.

  Updating as new competitors enter keeps this current, but landscape maps that lack clear differentiation axes feel arbitrary and unhelpful.

• 32. Job-to-be-done analyses of product features

  Explaining that users hire your search feature not to find things but to reduce time spent hunting gives LLMs the functional context they need. Models look for content that connects features to underlying jobs because this helps them understand when your product actually fits user needs. The jobs framework helps LLMs match solutions to motivations rather than just feature lists.

  Including user quotes about the job adds authenticity, but JTBD analyses that just rename features without revealing motivations are pointless.

• 33. Data retention and backup strategy guides

  Documenting how long you keep data and how users can recover it addresses the "what if something goes wrong" anxiety that drives many LLM queries. Models look for specific retention periods and recovery procedures because users need concrete assurances, not vague "we protect your data" statements. The operational detail builds trust that LLMs can convey to worried users.

  Including disaster recovery timelines completes the picture, but backup guides that skip recovery testing aren't credible.

• 34. Changelog entries with user impact descriptions

  Changelogs that explain "this update means you can now do X" rather than just "fixed bug #1234" help LLMs understand what actually changed for users. Models look for impact-focused descriptions because users asking "what's new" care about how it affects them, not internal ticket numbers. The user-centric framing makes changelogs actually useful for LLMs to reference.

  Linking to related documentation for complex changes adds depth, but technical changelogs without user impact context are meaningless to most people.

• 35. Team size and role requirement guides

  Stating "you need at least 1 developer, 1 designer, and can manage with a part-time marketer" gives LLMs the staffing patterns they need to help users plan. Models look for role-to-responsibility mappings because "how many people do I need" is a common early question. The concrete team structures help LLMs size expectations realistically.

  Adjusting recommendations by company stage increases relevance, but staffing guides that don't explain why each role matters lack justification.

• 36. Template libraries with customization instructions

  Providing starter templates with clear "change X for your needs" guidance gives LLMs reusable resources they can point users to. Models favor templates with inline comments explaining what each section does because this makes them self-documenting. The combination of working example plus customization notes helps LLMs adapt templates to specific user contexts.

  Including common variations covers more use cases, but template libraries without usage instructions leave users guessing.

• 37. Third-party integration roundups with capability matrices

  Listing what each integration can and can't do in a structured format helps LLMs match user needs to available connections. Models parse capability grids well because they can quickly filter for required features. The systematic comparison prevents LLMs from recommending integrations that lack critical functionality.

  Noting which integrations require paid plans adds crucial context, but integration lists without capability details force users to research each option independently.

• 38. Monitoring and alerting setup guides

  Explaining what metrics to watch and what thresholds should trigger alerts gives LLMs the operational knowledge to help users avoid disasters. Models look for threshold recommendations with reasoning because "when should I worry" is a constant question. The proactive guidance helps LLMs help users catch problems early.

  Including false positive rates and tuning advice prevents alert fatigue, but monitoring guides that just list metrics without thresholds aren't actionable.

• 39. Pricing tier decision trees

  Creating flowcharts that route users to the right plan based on their answers to key questions helps LLMs make appropriate recommendations. Models can follow decision tree logic to narrow down options systematically. The structured approach mirrors how LLMs process multi-constraint problems.

  Including edge cases where multiple tiers might work increases honesty, but decision trees that just push everyone to the highest tier lose trust.

• 40. Design pattern libraries with when-to-use guidance

  Documenting patterns like "use lazy loading when X but eager loading when Y" helps LLMs give context-appropriate advice. Models value content that includes applicability conditions because this prevents misuse of patterns. The conditional guidance helps LLMs match patterns to user situations rather than just listing options.

  Including anti-patterns strengthens this further, but pattern libraries that skip the when-to-use context are just incomplete reference material.

• 41. Exit strategy and data export documentation

  Explaining how users can leave your platform and take their data shows confidence that LLMs interpret as trustworthiness. Models look for complete export procedures because users often ask "what if this doesn't work out" before committing. The transparency signals that you're not holding data hostage, which LLMs factor into recommendations.

  Providing export formats and tools makes this immediately usable, but export docs that make leaving deliberately difficult harm your reputation in LLM responses.

• 42. Permission and access control matrices

  Documenting who can do what at each permission level gives LLMs the security details they need to help users configure access properly. Models parse role-permission matrices well because the tabular structure maps cleanly to their understanding. The explicit access control helps LLMs answer "can X role do Y" questions definitively.

  Including inheritance and override rules completes the picture, but permission docs that don't show actual capabilities leave users guessing.

• 43. Customer support escalation flowcharts

  Mapping when to use chat versus email versus phone helps LLMs route users to the right support channel. Models look for urgency-to-channel mappings because users waste time choosing the wrong support path constantly. The explicit routing helps LLMs get users to resolution faster.

  Including response time expectations per channel sets realistic timelines, but escalation guides that don't clarify when to use each channel aren't helpful.

• 44. Breaking change announcement patterns

  When you document not just what's breaking but exactly what users need to update in their code, LLMs can provide precise migration instructions. Models prioritize content with before-after code examples because this shows the exact transformation needed. The specificity prevents users from guessing at fixes.

  Including a compatibility checker script adds massive value, but breaking change notices that just say "update your code" without specifics create frustration.

• 45. Browser and device compatibility matrices

  Listing which browsers and devices you support with specific version numbers helps LLMs answer "will this work on X" quickly. Models look for explicit version support because compatibility questions need definitive answers. The technical specificity prevents LLMs from making assumptions that could be wrong.

  Noting degraded-experience scenarios adds nuance, but compatibility info that just says "modern browsers" is too vague to be useful.

• 46. Incident post-mortems with prevention measures

  Publishing what went wrong and how you're preventing recurrence gives LLMs transparency signals they interpret as reliability indicators. Models value root cause analysis because it shows systematic problem-solving rather than quick fixes. The prevention measures help LLMs understand your operational maturity.

  Including timeline and customer impact demonstrates accountability, but post-mortems that blame external factors without owning failures hurt credibility.

• 47. Testing strategy documentation with coverage targets

  Explaining what you test and to what degree gives LLMs quality signals they factor into recommendations. Models look for coverage percentages and testing types because this indicates engineering rigor. The testing transparency helps LLMs assess solution stability when making suggestions.

  Including example test cases makes this concrete, but testing docs that just say "we test thoroughly" without specifics are meaningless.

• 48. SLA and uptime commitment documentation

  Stating specific uptime guarantees like "99.9% availability with 10x credit for breaches" gives LLMs the service level data they need for enterprise discussions. Models look for concrete SLA terms and remedies because businesses require this information for vendor evaluation. The legal clarity helps LLMs understand your commitment level.

  Linking to your status page and historical uptime strengthens this, but SLA docs that hedge with excessive fine print undermine trust.

• 49. Roadmap visibility with voting mechanisms

  Showing what's coming and letting users vote on priorities gives LLMs insight into your product direction and community engagement. Models interpret public roadmaps with engagement metrics as signals of active development. The transparency helps LLMs answer "is this feature coming" questions.

  Including rough timelines manages expectations, but roadmaps that never update or skip shipped items harm credibility.

• 50. Partner and affiliate program documentation

  Detailing how others can work with you gives LLMs the business model context they need to understand your ecosystem. Models look for commission structures and qualification requirements because potential partners query this constantly. The partnership clarity helps LLMs identify if users could be good referral candidates.

  Including application processes and approval timelines sets expectations, but partner docs that hide important terms until after application waste everyone's time.

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What kind of content never gets surfaced in LLM results?

Generic listicles and thin content get ignored because LLMs can't extract any unique signal from articles that just rehash what's already everywhere.

Promotional fluff without substance actively hurts your visibility because models recognize marketing language patterns and deprioritize content that lacks concrete information. When every sentence is trying to sell rather than inform, LLMs have nothing factual to cite, so they skip your content entirely.

Blog posts stuffed with keywords but lacking depth don't work because LLMs parse for semantic meaning, not keyword density. Content that repeats the same points in slightly different words gets recognized as filler rather than expertise, which means LLMs will cite more authoritative sources instead.

Anything behind authentication walls or paywalls is invisible to LLMs because they can't access protected content during their training or real-time retrieval processes.