The Future of Topical Authority: Teaching Large Language Models to Trust You

Before we deep dive into the bigger picture of topical authority, it’s important to understand what it actually means. The following section breaks down the ‘what’ and ‘why’ and will help you gain a better understanding.

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Definitions

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What is topical authority?

Topical authority represents a website or brand’s credibility on a particular subject. This involves demonstrating deep expertise through comprehensive coverage of that particular topic. In effect, the website or brand would be considered a go-to source for information within that specific niche.  

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What is a Large Language Model (LLM)?

A large language model (LLM) is an advanced type of language model trained using self-supervised machine learning on vast amounts of text data. It is primarily designed for natural language processing tasks, particularly language generation. You can think of it as a highly skilled copywriter who has read through the internet archives, from blog posts and product descriptions to social media threads and can instantly produce relevant content based on the prompt it was given. By using generative pre-trained transformers (GPTs) such as OpenAI’s ChatGPT, Google’s Gemini, and Perplexity AI, these models can learn to understand context, predict meaning and respond in ways that align with the user’s intent. 

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What is trustworthiness?

Trust plays a critical role in both human-to-AI interaction and in establishing a website’s authority, whether in traditional search engine rankings or LLM-powered search platforms. Zachary W. Petzel & Leanne Sowerby describe it as the willingness to rely on an entity, whether a website or an AI system, based on the expectation that it will perform a particular action reliably, accurately, and without causing harm. This trust is even possible in the absence of full transparency or control. In that same context, trustworthiness is also a key part of Google’s helpful, people-first content framework E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness). 

Now that we have covered the basics, let’s explore this topic in more detail. 

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Understanding How LLMs Judge Content for Trust

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Pre-training Stage

Models are trained on vast amounts of internet data, but not all content is treated equally. Developers actively filter out low-quality or untrustworthy content during this stage. For example, OpenAI filtered pre-training data for GPT-4 to remove content from unreliable sources. Research highlighted in the paper“Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims” shows that authoritative sources, such as encyclopedic content and peer-reviewed materials are preferred in AI training as they provide trustworthy information and improve retrieval-based model performance. In contrast, unverifiable or low-quality sources are less likely to be included or be down-weighted during training.

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Post-training Retrieval/Answer Generation (RAG)

Large Language Models are increasingly designed to provide accurate, trustworthy answers by pulling in relevant and up-to-date information. One common method is Retrieval-Augmented Generation (RAG), which ranks and retrieves external sources based on relevance and authority before passing them to the model. This helps reduce the risk of the AI generating incorrect or made-up content. For example, Google’s Search Generative Experience (SGE) often includes citations, with most pointing to well-established, authoritative websites. LLMs are trained to favour reliable sources like encyclopedias and reputable databases, and their performance is assessed using both quantitative metrics and AI-driven tools that reflect human judgement. In some cases, advanced LLMs are even used to evaluate the quality of outputs from other models, a process known as "LLM-as-a-Judge". For example, features like Grounding with Google Search, Deep Research in Gemini and fact-checking tools in ChatGPT use this approach to verify information and ensure responses are clear, accurate and directly answer users' questions.

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Core Strategies for Building LLM Trust and Visibility

Now that we have covered the theoretical part, it’s time to look at possible strategies that would help you gain the trust of LLMs and gain visibility.

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Create Structured, Clear, and Comprehensive Content

• Organisation and Readability: LLMs excel at digesting well-organised information. They prefer content with a clear hierarchy (headings, subheadings) and concise sentences.
• Direct Answers and Q&A Formats: Since many LLM interactions are question-based, content should provide concise, direct answers to common queries, followed by more elaborate explanations, sing TL;DR summaries, lists, and FAQs as AI often extracts these snippets for direct answers.
• Comprehensive Topic Coverage: Aim for in-depth content that addresses different parts of a topic, related concepts and common user questions. Support this with a strong internal linking structure. By linking to pages that are semantically related, you help both users and search engine/LLM crawlers understand how your topics connect and make it easier for them to navigate your content. 
• Content Clusters: Organise content around a central "pillar page" with a broad overview, supported by interlinked articles covering specific subtopics. To learn more about content clusters, check out Carl Poxon’s Using Content Clusters for Human-Centric Conversions Brunch & Learn. 
• Specific Content Formats Favoured: LLMs are "citation machines" and prefer content like:
  
  • "Best Of" Lists
  • First-Person Product Reviews
  • Comparison Tables (especially Brand vs. Brand)
  • FAQ-Style Content
  • First Party Data
  • Opinion-Led Pieces with Clear Takeaways
  • Tools, Templates, and Frameworks
  • Data Segments

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Demonstrate E-E-A-T

• Showcase Authorship & Credentials: Clearly label authors, provide detailed bios, and highlight relevant qualifications, experience, or certifications.
• Original Research & Insights: Incorporate original data, statistics, and expert commentary.
• Real-World Experience: Product reviews and first-hand experience are critical, your content should clearly reflect genuine involvement. Familiarity increases trust, making potential customers more receptive to conversions over time.
• Transparency: Clearly stating sources for data and claims enhances perceived trustworthiness. “Content featuring original statistics and research findings can see 30-40% higher visibility in LLM responses.”
• Utilise Third Party Reviews: Highlight genuine ratings and feedback from trusted platforms like Google Reviews, TrustPilot and TripAdvisor as external trust signals to provide social proof and build credibility. 
• Knowledge Graphs & Entity Optimisation: LLMs build knowledge graphs of entities (people, organisations, products, concepts) and their relationships. Consistently referring to these by proper names helps LLMs understand and connect content to relevant searches, moving beyond simple keyword matching.

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Utilise ‘LLM Seeding’ and Off-Page Signals

• Get Featured in AI-Friendly Venues: LLMs are known to ingest large amounts of data from platforms like Wikipedia, Reddit, Quora, and other large data sources. Reddit, in particular, is cited more than any other source by LLMs, according to Semrush.
• Community and Third-Party Validation: Contributing high-quality answers on platforms like Quora or maintaining well-sourced Wikipedia pages can make your content part of training data or a go-to reference for models. 
• Digital PR & Mentions: Digital PR is considered "a thousand times more important" by SEOs like Lily Ray in the generative AI landscape as LLMs seek consensus across trustworthy sources. 
• User-Generated Content (UGC): Actively encouraging detailed reviews on comparison and review sites can influence how your product is described and cited by LLMs.
• Social Platforms: Use clear, searchable language on platforms like X (for educational threads), YouTube (descriptive titles, detailed descriptions, accurate captions), Pinterest (rich descriptions), and Instagram (captions, alt text, hashtags).

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Ensure AI Can Access Your Content

• Make sure you are not unintentionally blocking AI crawlers using robots.txt
• Use tools like the AI Bot Access Analyzer to see if your website is discoverable by AI bots.

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Use AI Responsibly & Human-First

• Have a human oversight: Don’t give AI permissions to publish unreviewed content. Human input can provide a unique perspective, ethical judgement and first-hand experience that AI currently struggles to generate authentically. Reflect Digital is a human-first AI SEO agency.
• Fact-Check: Incorrect statistics can undermine the trust you are building with your audience.
• Transparency: Be transparent about your use of AI. 
• Authenticity: High-quality, user-centered content will outstand algorithm changes. 
• Update your information: Update your high-value pages to ensure LLMs don’t use outdated information.

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Benefits of Teaching LLMs to Trust You

By successfully building trust with LLMs, you can significantly enhance your online presence. Authoritative content not only improves traditional rankings as Google favours credible sources, but also accelerates your visibility with high authority pages. Through demonstrating expertise, you foster user trust which can lead to an increase in CTR (Click Through Rate), while at the same time, brand mentions in ChatGPT Search, AI Mode etc. can position your site as a trusted source of information, much like receiving a recommendation from an influential third party. In theory, LLMs are designed to prioritise the best, most relevant answers over conventional rankings, which gives smaller brands a fairer chance to be cited. As AI adoption grows, success is gradually shifting from traditional metrics like CTR to measuring topical authority and citation frequency within generative search tools. 

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Ethical Considerations and Challenges in Trust

Despite ongoing efforts to build trust, LLMs are facing key ethical challenges, namely bias in training data, which can lead to harmful stereotypes. Privacy risks also arise from unintentional data retention and the potential to leak sensitive information. Accountability is unclear with no single party being responsible for the spread of misinformation. Models are known to hallucinate and confidently present false claims, as well as mimic user opinions. This in turn, reduces the objectivity and usefulness of the responses. Tackling these issues is essential to ensure that LLMs are trusted and used responsibly.

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Conclusion

Building LLM trust and visibility on Google’s SERP involves creating a clear, comprehensive content that applies the E-E-A-T framework, uses structured data, and gains brand mentions on platforms frequently referenced by AI models. It is essential to ensure that LLM crawlers can access your site and to apply AI responsibly with human oversight and fact-checking. This approach supports traditional search rankings while also increasing the likelihood that generative AI cites your content.  

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