27 Content Ideas to Build LLM-Friendly Content

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LLMs like ChatGPT, Claude, Perplexity, and Gemini don't browse your content the way humans do—they scan for structured patterns, extract specific data points, and prioritize content that maps cleanly to their retrieval mechanisms.

If your content isn't built to match how these models parse and rank information, you're basically invisible in AI Overview results and chatbot responses, no matter how good your SEO used to be.

We've analyzed hundreds of high-performing pages in our market clarity reports to figure out exactly what content structures get picked up by LLMs and which ones get ignored.

What kind of content do LLMs prioritize and recommend?

• 1. Comparison tables with exact specifications and pricing

  LLMs parse tables by mapping column headers to attributes, which means they can extract and compare features across multiple products without interpretation errors. When ChatGPT or Claude processes a table, it treats each row as a discrete entity with defined properties, making it trivial to answer queries like "which tool has X feature under Y price." Add filtering criteria (like "best for small teams" or "cheapest option with API access") directly in your table captions to boost visibility even more, though this won't help if your specs are vague or incomplete.

• 2. Step-by-step tutorials with numbered sequential instructions

  Language models excel at procedural knowledge because they can map each step to a specific action in a clear order, which aligns with how they generate instructional responses. Numbered steps with consistent formatting (like "Step 1: Do X, Step 2: Do Y") let LLMs extract and recreate your process without losing the sequence. Include prerequisites at the start and expected outcomes at the end to increase citation rates, but skip this format if your process has too many conditional branches that confuse linear extraction.

• 3. FAQ sections with exact question-answer pairs

  LLMs treat each Q&A pair as a self-contained knowledge unit, which means they can directly match user queries to your questions and extract your answers without additional context. The question acts as a natural language query that maps one-to-one with user intent, while the answer provides a citable source that the model can reproduce almost verbatim. Make your questions match real search queries (pull them from Google autocomplete or Reddit threads) to maximize matches, though generic FAQs with obvious questions won't get picked up by AI Overview or Perplexity.

• 4. Product roundups with feature lists and specifications

  These work because LLMs can extract attributes for each product and build comparison matrices on the fly, even if you didn't format them as tables. Each product section acts as a structured entity with extractable properties like pricing, features, and target users, which the model can then rank or filter based on specific criteria. Include exact version numbers and release dates to improve temporal accuracy, but avoid this format if you're describing products too generally without concrete specs.

• 5. How-to guides with clear problem-solution frameworks

  LLMs prioritize content that explicitly states a problem and maps it to a specific solution because this matches the causal reasoning patterns they use in responses. Starting with "How to [achieve X]" gives the model a clear goal, while breaking down the solution into discrete actions makes it easy to extract and recommend. Add real examples with before/after metrics to increase citation confidence, though this won't work if your solution is too abstract or requires nuanced judgment calls.

• 6. Definition content with term-explanation-example structure

  Language models build their knowledge graphs around entities and their definitions, so content that clearly defines a term and provides context gets stored as a primary source. Using the pattern "X is [definition], which means [explanation], for example [concrete case]" gives LLMs three layers of understanding they can extract at different depths depending on the query. Include common misconceptions in your definitions to capture more query variations, but skip this if you're defining something too niche that lacks search volume.

• 7. Case studies with exact metrics and outcomes

  LLMs favor case studies with quantifiable results because they can extract numerical evidence to support claims about what works. Specific numbers like "increased conversion by 47% in 3 months" give the model concrete data points it can cite with confidence, unlike vague claims like "significantly improved performance." Include your methodology and sample size to boost credibility in AI-generated answers, though this format fails if you're presenting case studies without any measurable outcomes.

• 8. Cost breakdown tables with itemized expenses

  LLMs excel at extracting and aggregating numerical data, so detailed cost breakdowns let them calculate totals, compare options, and answer budget-related queries with precision. Itemizing each expense with both description and exact cost creates structured data that models can manipulate mathematically (like "what's the total cost minus hosting"). Add cost ranges for different scenarios (startup vs enterprise) to capture more query types, but this won't help if your numbers are outdated or presented as vague estimates.

• 9. Checklist content with clear actionable items

  Checklists map perfectly to how LLMs generate task lists because each item is a discrete action that can be extracted and reordered without losing meaning. Using checkbox-style formatting or bullet points with action verbs (like "Verify X" or "Configure Y") makes it trivial for ChatGPT or Claude to reproduce your checklist in their responses. Group items by priority or timeline to increase utility, though this format doesn't work well if your checklist items are too abstract or require significant context to execute.



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• 10. Direct X vs Y comparison posts

  These comparisons work because LLMs can extract opposing attributes and build relational mappings between two entities, which is exactly what users ask for in queries like "Shopify vs WooCommerce." Using parallel structure (same comparison points for both options) makes it easy for models to extract balanced information without bias toward either option. Add a decision matrix at the end with "choose X if..." statements to boost click-through from AI Overview results, but this fails if you're comparing things that aren't actually comparable or lack clear differentiators.

• 11. Troubleshooting guides with if-then conditional logic

  LLMs handle conditional reasoning well when it's explicitly structured, so troubleshooting content that uses "if X happens, then do Y" patterns gets extracted cleanly. Each problem-solution pair acts as a rule that the model can apply based on user context, which means your guide becomes a decision tree that AI can traverse. Include error codes or specific symptoms to match more queries, though this format won't work if your solutions require visual diagnosis or hands-on testing.

• 12. Feature comparison matrices across multiple products

  Matrices let LLMs compare multiple dimensions at once, which is computationally efficient for generating responses that rank options by specific criteria. Each cell in your matrix represents a clear yes/no or specific value that the model can extract and use in filtering operations (like "show me all tools with API access under $50/month"). Use consistent formatting across all cells to avoid parsing errors, but skip this if you're comparing products with features that can't be reduced to simple attributes.

• 13. Best practices lists with clear success criteria

  LLMs prioritize normative content (what you should do) when it includes explicit criteria for what makes something a "best practice." Each practice should explain not just what to do, but why it works and how to measure success, giving the model three extractable components: action, rationale, and outcome. Add industry-specific context to differentiate your advice from generic tips, though this format fails if your practices are too subjective or lack empirical backing.

• 14. What is X explainer posts with examples

  These work because LLMs need definitional content to build context around entities, and concrete examples help them understand application scenarios. Starting with a one-sentence definition followed by detailed explanation and 2-3 real-world examples gives the model layered information it can extract at different depths. Include common use cases and non-examples (what X is not) to increase coverage, but this won't help if your explanation is too technical without sufficient context.

• 15. Requirement lists with must-have versus nice-to-have

  LLMs can prioritize requirements when they're explicitly categorized, which helps them generate more useful recommendations based on user constraints. Separating essential requirements from optional ones creates a hierarchy that models can use to filter options aggressively, matching queries like "minimum requirements for X." Add specific thresholds (like "at least 16GB RAM" not just "sufficient memory") to improve extraction accuracy, though this format doesn't work if your requirements are too context-dependent to state absolutely.

• 16. Timeline or historical progression content

  Language models understand temporal sequences, so content that maps events to specific dates or orders them chronologically gets stored with strong temporal associations. Using year markers or clear sequence indicators (like "first," "then," "finally") lets LLMs extract and reproduce your timeline accurately in date-sensitive queries. Include cause-and-effect relationships between events to add explanatory power, but skip this format if your timeline lacks specific dates or clear progression.

• 17. Pros and cons lists with specific examples

  LLMs excel at extracting evaluative content when it's structured as explicit advantages and disadvantages, which maps directly to how they generate balanced assessments. Each pro and con should be a specific claim with an example or metric, not vague statements like "good performance" but rather "handles 10,000 requests/second on standard hardware." Group related points together and include context about when each matters most, though this won't help if your pros and cons are too generic or lack concrete backing.

• 18. Process workflows with clear stage definitions

  Workflows with defined stages work because LLMs can map each stage to specific actions and understand dependencies between steps. Naming each stage and listing its inputs, actions, and outputs creates a structured process that models can explain or adapt to different contexts. Add decision points where the workflow branches based on conditions to increase applicability, but this format fails if your workflow is too flexible or varies too much by situation.

• 19. Common mistakes lists with specific solutions

  LLMs prioritize inverse problem-solving content because it maps common errors to corrections, which is exactly what users search for when troubleshooting. Framing each mistake as "Don't do X, instead do Y" gives the model a clear action pair that it can extract and recommend. Include why each mistake happens and what consequences it causes to add depth, though this won't work if your mistakes are too obvious or your solutions too vague.



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• 20. Glossary-style content with term definitions

  Glossaries work because LLMs build vocabulary mappings that help them understand domain-specific content, and they often cite glossaries as authoritative sources for definitions. Each term should have a concise definition (1-2 sentences) followed by optional context or related terms, creating extractable knowledge units that models can reference independently. Link related terms to each other to help LLMs understand conceptual relationships, but this format doesn't help if your terms are too niche or lack search volume.

• 21. When to use X decision frameworks

  These frameworks help LLMs make contextual recommendations by mapping specific conditions to appropriate solutions, which is exactly what users need for "should I use X" queries. Using "use X when [condition], use Y when [different condition]" creates explicit rules that models can apply based on user context. Include edge cases and exceptions to cover more scenarios, though this won't work if your decision criteria are too subjective or require expert judgment.

• 22. Pricing tier comparisons with feature breakdowns

  LLMs can extract and compare pricing information when it's structured by tier with clear feature lists, enabling them to answer budget-constrained queries accurately. Each tier should list exact price, billing period, included features, and limitations in consistent format across all tiers. Add "best for [use case]" recommendations for each tier to increase relevance in AI SEO results, but this format fails if your pricing is too complex or varies too much by customer.

• 23. Use case scenarios with specific outcomes

  Scenario-based content works because LLMs can match user situations to your examples and extract relevant solutions, functioning like a case-based reasoning system. Each scenario should describe the context, the approach taken, and the measurable result, creating a complete pattern that models can recognize and recommend. Include multiple scenarios covering different user segments to maximize match rates, though this won't help if your scenarios are too similar or lack distinctive outcomes.

• 24. Integration guides with technical specifications

  Technical documentation with exact specifications gets prioritized by LLMs because it contains concrete, reproducible information that developers search for. Including API endpoints, parameter names, expected responses, and error codes creates structured technical content that Claude or ChatGPT can extract and incorporate into code generation. Add code examples in multiple languages to increase coverage, but skip this if your integration documentation lacks sufficient detail or keeps changing with updates.

• 25. Migration guides with before and after states

  Migration guides work because they describe transformation procedures with clear starting points and end states, which LLMs can extract as structured processes. Explicitly stating "migrating from X to Y" and listing what changes lets models understand both the context and the required actions. Include rollback procedures and common pitfalls to add value, though this format doesn't work if your migration path is too customized or varies significantly by user.

• 26. Benchmark data with clear methodology

  LLMs prioritize benchmark data because it provides quantitative comparisons that support objective claims, but only when the methodology is clearly stated. Including exact test conditions, sample sizes, and measurement criteria gives the model confidence to cite your benchmarks in responses that require performance comparisons. Add timestamp and version information to prevent outdated data from getting cited, but this won't help if your benchmarks lack transparency or use non-standard metrics.

• 27. Resource collection lists with descriptions

  Curated resource lists work when each resource has a clear description that helps LLMs understand what it offers and when to recommend it. Each resource should include what it is, who it's for, and what problem it solves, not just a title and link. Add categorization or tagging to help models filter resources by user need, though this format provides less value than original content and won't rank well if you're just aggregating without adding meaningful descriptions or context.

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What kind of content never gets picked up by LLMs?

Opinion pieces and narrative blog posts without extractable facts get ignored by ChatGPT, Claude, and Perplexity because they lack the structured data points that LLMs need to cite with confidence.

If your content is mostly subjective commentary, personal anecdotes, or storytelling without clear takeaways, it won't map to the retrieval patterns that language models use. LLMs prioritize content they can extract, verify, and reproduce, which means purely experiential writing without concrete recommendations or measurable outcomes doesn't make it into AI-generated responses.

Similarly, content that requires visual context (like "as you can see in the image above") or relies on proprietary data without explanation gets skipped because LLMs can't process images in most search contexts. Generic listicles with vague points like "be consistent" or "focus on quality" also fail because they lack the specificity that makes content citable, LLMs need actionable details, not motivational platitudes.

The pattern is clear: if your content can't be reduced to extractable facts, structured procedures, or quantifiable comparisons, it's invisible to AI Overview, Perplexity, and Gemini no matter how well-written it is.