Authority Signals for LLMs: ChatGPT, Claude & Gemini

As ChatGPT, Claude, and Gemini become primary research tools for millions of users, traditional SEO is no longer enough. When a user asks an AI assistant for a recommendation, your business either appears in the answer – or it doesn’t exist. This guide explains exactly how each AI engine evaluates authority, and what you can do about it.

In this article

1. The authority problem in the age of AI answers
2. How LLMs actually evaluate authority
3. Authority signals for ChatGPT (OpenAI)
4. Authority signals for Claude (Anthropic)
5. Authority signals for Gemini (Google)
6. Cross-engine signal comparison
7. Beyond traditional SEO: GEO strategies
8. Citations, mentions and digital PR
9. Content structure for LLM comprehension
10. Structured data and schema for AI
11. Monitoring your LLM authority
12. The Kaleto authority playbook

1. The Authority Problem in the Age of AI Answers

For two decades, SEO was the language of discoverability. Rank on page one of Google, and the visitors follow. But something fundamental has shifted. When a user asks ChatGPT “what’s the best project management tool for remote teams?” or asks Gemini “which agency should I hire for B2B content marketing?” — the answer they receive isn’t a list of blue links. It’s a confident, synthesised recommendation. Your company either appears in that recommendation, or it doesn’t exist.

This is the core challenge of what is now called Generative Engine Optimisation (GEO): optimising not for a ranking algorithm, but for a probabilistic language model trained on vast corpora of human knowledge. The rules are different. The signals are different. And crucially, each major LLM has its own approach to determining who gets cited, referenced, and recommended.

The businesses that will win in the next five years are those that build genuine, multi-layered authority that LLMs can recognise, retrieve, and confidently surface.

What is “LLM Authority”? LLM authority refers to the degree to which a large language model considers your brand, content, or expertise as a trustworthy, citable source when generating responses. Unlike PageRank, it is not a single score — it is an emergent property of how comprehensively and consistently your entity appears across the sources that LLMs draw upon during training and real-time retrieval.

2. How LLMs Actually Evaluate Authority

Before diving into platform-specific signals, it’s essential to understand the underlying mechanism. LLMs do not index the web the way Google does. They don’t follow links or calculate PageRank. Instead, their “knowledge” of who is authoritative emerges from the statistical patterns in their training data — and increasingly, from real-time retrieval systems bolted onto that base.

The three layers of LLM knowledge formation

Layer 1: Training data (parametric memory)

During pre-training, LLMs consume enormous corpora of text — web crawls, books, academic papers, forums, news archives. The patterns in this data determine what the model “believes” about the world. Entities that appear frequently, consistently, and in high-quality contexts become embedded as authoritative in the model’s weights. This is the hardest layer to influence directly, but it is where long-term authority compounds most powerfully.

Layer 2: Retrieval-Augmented Generation (RAG)

Modern LLMs increasingly supplement their parametric knowledge with real-time retrieval — querying live search indexes, knowledge bases, or curated document stores before generating a response. ChatGPT with web browsing, Gemini’s integration with Google Search, and Claude’s tool connections all operate on this principle. Here, real-time indexability and source quality matter enormously.

Layer 3: RLHF and fine-tuning signals

Through Reinforcement Learning from Human Feedback (RLHF), models learn to prefer certain types of sources and framings. If human raters consistently reward responses that cite academic research, established publications, and expert commentary, the model learns to weight those signals.

The concept of entity salience

One of the most useful concepts for understanding LLM authority is entity salience — how prominently and distinctly an entity (your brand, a concept, a person) is represented in the model’s knowledge. A highly salient entity has clear, consistent, multi-faceted representations. Achieving entity salience is the north star of GEO strategy.

“Influence over an LLM’s recommendations is not purchased — it is earned through repeated, high-quality presence in the information ecosystem that shapes the model’s understanding of the world.”

3. Authority Signals for ChatGPT (OpenAI)

ChatGPT’s authority model is shaped by OpenAI’s training choices, its use of Bing’s search infrastructure for web-enabled queries, and the preferences learned through millions of human feedback interactions.

Training data prevalence

OpenAI’s models are trained on Common Crawl, curated web data, books, and other large corpora. Domains that appear frequently in high-quality contexts — linked from respected publications, discussed in academic papers, cited in long-form journalism — are more likely to have strong parametric representation. The mechanism here is not domain authority as Google measures it, but narrative density: how thoroughly your entity is woven into the fabric of online discourse.

Key authority signals for ChatGPT

• Bing search rankings: ChatGPT’s browsing capability is powered by Bing. Ranking well in Bing directly improves ChatGPT citation rates. Bing weights brand signals — brand search volume and name recognition — more heavily than Google.
• Wikipedia presence: Wikipedia is heavily weighted in OpenAI’s training data. A well-sourced Wikipedia entry significantly boosts entity recognition and factual accuracy in ChatGPT outputs.
• Reddit and forum mentions: OpenAI has a data partnership with Reddit. Community recommendations, brand mentions in specialist subreddits, and peer discussion strongly influence model associations.
• News publication coverage: Coverage in established news outlets — particularly those with high editorial standards — signals credibility. Frequency and recency both matter.
• Consistent entity naming: ChatGPT identifies entities partly through name consistency. Ensure your brand name, product names, and key people are referenced consistently across all public-facing content.
• Custom GPTs and the plugin ecosystem: Building and publishing Custom GPTs in the GPT Store exposes your brand and content directly to the OpenAI ecosystem — a novel signal channel most businesses haven’t yet exploited.

Key insight — the Reddit signal: OpenAI’s $60M data licensing deal with Reddit means that genuine community discussion of your brand on Reddit carries disproportionate weight in ChatGPT’s associations. Organic, value-adding participation in relevant subreddits — by your team members contributing genuine expertise — is a legitimate and underexploited authority signal.

4. Authority Signals for Claude (Anthropic)

Claude, developed by Anthropic, has a distinctive epistemic character. Its training prioritises accuracy, nuance, intellectual honesty, and calibrated uncertainty. These are not just philosophical commitments — they are operationally significant for authority signal strategy.

The Anthropic epistemic model

Claude is specifically trained to be cautious about making confident factual claims without strong evidence, to distinguish between widely accepted views and contested ones, and to caveat recommendations appropriately. This means that sources which demonstrate epistemic rigour — acknowledging limitations, citing evidence, distinguishing opinion from fact — are likely to be weighted more favourably.

Key authority signals for Claude

• Academic and research citations: Claude places high weight on content that cites peer-reviewed research, published studies, or expert consensus. Link your claims to primary sources wherever possible.
• Epistemic honesty: Content that acknowledges nuance, competing views, and uncertainty aligns with Claude’s training values. Balanced, rigorous content outperforms marketing-speak.
• Long-form depth: Claude tends to favour comprehensive, well-structured content over brief summaries. Deep-dive articles, whitepapers, and detailed guides signal genuine expertise.
• Named author credentials: Clearly attributing content to named experts with verifiable credentials, institutional affiliations, or track records strengthens Claude’s confidence in citing it.
• Constitutional alignment: Content that is factually accurate, non-manipulative, and ethically produced aligns with Anthropic’s constitutional AI principles — and is less likely to be downweighted.

Claude’s unique emphasis — calibrated uncertainty: Content that is appropriately humble and evidence-backed may actually perform better than content that makes strong, unqualified claims. Write like a trusted expert who acknowledges the limits of their knowledge. Use phrases like “research suggests,” “in most cases,” and “it depends on.” Claude finds this more credible, not less.

The expert author signal

Claude gives considerable weight to named authorship and credential verification. Building a public-facing team of visible experts — with LinkedIn profiles, published work, speaking engagements, and clear areas of expertise — creates attribution chains that Claude can draw on. Ghost-written, anonymous, or committee-authored content is at a structural disadvantage here.

5. Authority Signals for Gemini (Google)

Of the three major LLMs, Gemini has the most direct and transparent connection to a search authority system. Built by Google and integrated into Google Search, Workspace, and its own standalone interface, Gemini inherits — and extends — decades of Google’s thinking about what makes a source trustworthy.

Google’s E-E-A-T framework, applied to Gemini

Google’s Search Quality Rater Guidelines have long featured E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. Gemini operationalises E-E-A-T at the model level — because Gemini’s retrieval layer is Google Search, and Google Search has spent years quantifying E-E-A-T signals.

Key authority signals for Gemini

• Google Search rankings: Gemini’s retrieval is powered by Google’s search index. Ranking well for informational and commercial queries in Google directly determines Gemini citation eligibility.
• Google Business Profile: A complete, verified GBP with reviews, accurate NAP data, and active posting is a direct trust signal for local and business entity queries.
• Google Knowledge Graph presence: Google’s Knowledge Graph stores structured entity data. Appearing in the Knowledge Graph — with accurate, complete information — dramatically improves Gemini entity recognition.
• YouTube and video content: Google owns YouTube. Video content — especially educational, expert-led content — is indexed and factored into Gemini’s understanding of entity expertise.
• Google Scholar citations: For B2B, healthcare, legal, and professional services, appearing in Google Scholar through original research or whitepapers carries significant weight.
• First-party Google products: Being listed, rated, or reviewed across Google products (Maps, Shopping, Reviews, Play Store) contributes to a holistic entity authority profile.

Key differentiator — the Knowledge Graph: Google’s Knowledge Graph is arguably the single most powerful entity authority system in existence. Getting your brand, key executives, products, and concepts into the Knowledge Graph — through Wikipedia entries, structured data, Google Business Profile, and consistent NAP citations — creates a foundation that Gemini draws on for every relevant query. This is the GEO equivalent of founding-level domain authority.

Gemini’s multimodal authority signals

Unlike ChatGPT and Claude, Gemini has native multimodal capabilities. Optimised images with proper alt text and structured metadata, video content on YouTube with transcripts and chapters, and visual assets that appear in Google Image Search all contribute to a richer entity profile that Gemini draws upon.

6. Cross-Engine Signal Comparison

Understanding the similarities and differences between how each LLM evaluates authority helps you prioritise efforts and identify where universal strategies apply versus where platform-specific tactics are needed.

The strategic implication: a unified GEO strategy should have a strong universal foundation — Wikipedia, quality content, expert authorship, structured data — with targeted platform-specific layers built on top.

7. Beyond Traditional SEO: GEO Strategies

Traditional SEO operates on a deterministic model: optimise these elements, achieve these rankings, receive this traffic. GEO operates on a probabilistic model: increase the likelihood that, when a relevant query is processed, a language model will retrieve and cite your content, entity, or perspective.

The shift from keyword-first to entity-first thinking

Rather than asking “what keywords do I want to rank for?” ask your AI search agency “what entities do I want an LLM to associate with my brand, and how thoroughly can I establish those associations?” This means building comprehensive, interconnected content around the core concepts, problems, and solutions that define your category — not just targeting individual search queries.

Topical authority depth

LLMs are more likely to cite sources that have comprehensive coverage of a topic rather than a single strong piece. A content hub with 30 interconnected articles covering every dimension of a subject — from introductory explainers to advanced technical deep-dives — builds topical authority that LLMs recognise as domain expertise.

Six high-impact GEO strategies

1. Build a semantic entity web. Create interlinked content that establishes your brand as a node in a wider knowledge graph of concepts, people, and organisations relevant to your industry.
2. Publish primary data. Original surveys, research reports, and datasets that other publications cite establish you as a knowledge originator — the kind of entity LLMs are trained to surface.
3. Maintain information freshness. Regularly updated content signals that your entity is actively maintained. LLMs with RAG capabilities weight recency, especially for fast-moving topics.
4. Target definition-style queries. When users ask “what is [concept]?” or “how does [thing] work?” LLMs need a source to draw on. Positioning your content as the canonical definition of concepts in your space is a high-value GEO strategy.
5. Create referenceable statistics. Publishing original research with compelling, citable statistics makes your content the natural reference point when LLMs generate content involving those numbers.
6. Be present at category inception. When a new category, technology, or trend emerges, the brands that publish authoritative content first — and comprehensively — tend to become the training-data reference for that topic across subsequent model iterations.

8. Citations, Mentions and Digital PR

In traditional link-building, the currency is the hyperlink. In GEO, the currency is the unstructured mention — the co-occurrence of your brand name with relevant concepts, problems, and expertise in contexts that LLMs consider high-quality.

Tier 1: Editorial media coverage

Being cited in tier-one media — The Guardian, The Economist, TechCrunch, the FT, and serious industry publications — creates the kind of authoritative co-occurrence that all three major LLMs weight heavily. This is not about volume; a single, substantive piece in a respected publication typically outweighs dozens of press release pickups. The goal is genuine news value: original research, expert commentary on major events, or industry-first announcements.

Tier 2: Expert author placement

Contributing expert articles, opinion pieces, and guides to respected industry publications — with bylines linking to your brand — creates attribution chains. Platforms including Forbes, Harvard Business Review, McKinsey Insights, specialist trade publications, and academic preprint servers like SSRN all carry significant weight.

Tier 3: Peer and community endorsement

Recommendations in professional communities — LinkedIn posts from respected practitioners, threads on industry Slack groups, Hacker News discussions, and niche Reddit communities — create the distributed, peer-endorsed signal that LLMs interpret as community consensus.

The mention-without-link opportunity: Traditional SEO required links to carry authority. GEO changes this calculus. An unlinked mention of your brand in a high-quality article — “according to [Your Brand], which publishes research on X” — is a meaningful authority signal for LLMs. This makes digital PR campaigns that generate brand mentions without necessarily generating links far more valuable in a GEO context. Pursue coverage for the mention, not just the backlink.

Academic and research citation networks

If your business operates in a space where academic research is relevant — healthcare, finance, technology, climate, education — getting your original research cited in academic papers creates citation chains that LLMs, particularly Claude, treat as high-authority signals.

Podcast and video mentions

Increasingly, LLMs have access to transcripts from major podcasts and YouTube videos. Being interviewed on respected industry podcasts or mentioned in popular video content creates audio-to-text entity associations that feed into LLM training pipelines — a rapidly growing signal channel.

9. Content Structure for LLM Comprehension

LLMs do not read content the way humans do. They process token sequences and build statistical representations. This has specific implications for how content should be structured to maximise LLM comprehension, retention, and citation likelihood.

Writing for model extraction

LLMs are most likely to extract and cite content that contains clearly stated, self-contained factual claims. A sentence like “According to Kaleto Digital’s 2025 B2B Content Study, 73% of purchase decisions in enterprise software are influenced by vendor thought leadership” is highly extractable. A vague paragraph about the importance of content marketing is not. Write with the extractable claim as your fundamental unit of content.

Structural elements that aid LLM comprehension

• Clear, declarative headings that state what a section covers — not clever or ambiguous ones
• Definitions of key terms at the point where they are first introduced
• Numbered or bulleted lists for multi-part explanations — these parse more cleanly than dense prose
• Named entities (your brand, people, organisations) used consistently throughout — not replaced with pronouns
• Statistics and data points presented in a single, clear sentence with source attribution
• Explicit context-setting: “This guide covers X for Y audience in Z context”
• Summary or TL;DR sections that distil key takeaways — models use these as high-confidence extraction points
• FAQ sections that mirror the actual questions users ask LLMs
• Consistent use of canonical brand and product names, not abbreviated or colloquial variants

The FAQ page as LLM answer template

One of the highest-leverage GEO content strategies is the comprehensive FAQ page structured around questions that users actually ask AI assistants. If a user asks Gemini “what are the best practices for B2B content marketing?” and your FAQ page contains a clear, well-written answer to exactly that question, Gemini’s retrieval system is more likely to surface your content as the source.

Explicit attribution and provenance

LLMs are more confident citing content when authorship and provenance are clear. Every piece of content should have: a named author with credentials, a clear publication date, an organisation attribution, and ideally links to the primary sources that inform the content. This creates a traceable citation chain that the model can evaluate.

10. Structured Data and Schema for AI Visibility

Structured data — JSON-LD schema markup — was originally designed to help search engines understand content. In the GEO context, it serves a second, increasingly important function: it provides machine-readable entity definitions that LLMs and their retrieval systems can use to build richer entity representations.

Priority schema types for LLM authority

• Organisation schema: Define your brand, founding date, logo, contact details, social profiles, and parent/subsidiary relationships
• Person schema: For each key expert on your team — name, job title, credentials, published works, and organisation affiliation
• Article and BlogPosting schema: Author, publication date, modification date, headline, and article body — makes content extractable and attributable
• FAQPage schema: Directly maps your Q&A content to the question-answer format that LLMs process and retrieve
• HowTo schema: Step-by-step instruction content marked up in a way that models can extract procedural knowledge
• Product and Service schema: For commercial entities, clearly defining what you offer in machine-readable terms
• BreadcrumbList schema: Establishes content hierarchy and topical relationships that help models understand your site’s knowledge architecture
• SpeakableSpecification: Marks up content specifically designed to be read aloud — increasingly relevant as voice AI interfaces expand

Wikidata as a structured authority foundation

Wikidata is the structured data backbone of Wikipedia and feeds directly into Google’s Knowledge Graph — which in turn is a primary data source for Gemini and a secondary source for ChatGPT and Claude. Creating and maintaining a Wikidata entry for your organisation, with accurate, well-referenced property values, is one of the highest-ROI GEO activities available. It is free, technically accessible, and creates a machine-readable entity definition that propagates across the entire LLM ecosystem.

The JSON-LD priority stack: If you can only implement a limited amount of schema markup, prioritise in this order: (1) Organisation with sameAs properties linking to all official profiles, (2) Person schema for each named expert author, (3) Article schema with author and datePublished on every content piece, (4) FAQPage on any Q&A content. This stack creates the entity-author-content attribution chain that LLMs rely on for credible citation.

11. Monitoring Your LLM Authority

Unlike traditional SEO, where rank tracking tools provide clear, objective data, LLM visibility is probabilistic and harder to measure. But measurement is not impossible — it requires a different toolkit and a different mindset.

Prompt-based monitoring

The most direct method is systematic prompt testing: regularly asking each major LLM a set of target queries and analysing whether your brand is cited, how it is described, and what sentiment is expressed. This can be done manually for small-scale monitoring or automated via the APIs of each platform for larger programmes. Key query types to test include category-defining queries (“what are the best tools for X?”), problem-solution queries (“how do I solve Y?”), and direct brand queries (“what do you know about [Brand]?”).

Citation tracking

Tools such as Brand24, Mention, and Prowly can monitor online mentions of your brand across the web — including the kinds of high-quality publications and forums that feed into LLM training data. While this does not directly measure LLM visibility, it provides a leading indicator: the more high-quality, contextually relevant mentions your brand generates, the more likely it is to achieve stronger LLM representation over time.

Emerging GEO-specific platforms

A new category of tools specifically designed to track LLM brand visibility is emerging, including Profound, Otterly.ai, and Semrush’s AI-specific tracking features. These platforms systematically query multiple LLMs with relevant prompts and track how frequently and in what context your brand is cited. This category will become a standard part of the digital marketing measurement stack within the next 18 to 24 months.

What to measure

• Citation rate: percentage of relevant queries in which your brand is mentioned
• Citation rank: where in the response your brand appears (first mention vs. later)
• Sentiment: how your brand is described — positively, neutrally, or with caveats
• Competitive share of voice: how often your brand is cited vs. key competitors
• Entity accuracy: whether the model’s description of your brand, products, and people is accurate
• Correction speed: how quickly LLMs update their representation of your brand after major news events

12. The Kaleto Authority Playbook

Synthesising everything above, here is the strategic framework for businesses seeking to build meaningful authority across the LLM ecosystem. These activities should be treated as ongoing investments rather than one-time campaigns.

Foundation layer (months 1–3)

1. Entity definition audit. Review how each major LLM currently represents your brand. Identify inaccuracies, gaps, and missing entity associations.
2. Wikipedia and Wikidata setup. Create or improve Wikipedia and Wikidata entries for your organisation and key executives. Ensure all entries are well-sourced and consistently updated.
3. Schema markup implementation. Implement Organisation, Person, Article, and FAQPage schema across your entire web presence.
4. Named expert programme. Identify two to four internal experts who will serve as public-facing thought leaders. Build their online presences with content, credentials, and consistent attribution.
5. Content architecture audit. Evaluate your existing content against topical authority requirements. Identify gaps and prioritise new content for maximum GEO impact.

Authority building layer (months 3–9)

1. Original research programme. Publish at least one original research report or survey per quarter. Promote it to generate citations in publications across all three LLMs’ training data sources.
2. Tier-1 media outreach. Run a sustained PR programme targeting editorial coverage in publications that all three LLMs weight highly.
3. Community engagement. Establish a consistent, value-adding presence in relevant Reddit communities, industry Slack groups, and LinkedIn professional communities.
4. YouTube and podcast authority. Launch or grow a video and audio presence with consistent expert commentary. Target podcast appearances on shows with strong editorial reputations.
5. FAQ content hub. Build a comprehensive FAQ and knowledge base structured around the questions your target audience asks LLMs.

Compounding layer (months 9+)

1. LLM monitoring programme. Implement systematic prompt monitoring across ChatGPT, Claude, and Gemini. Set benchmarks and track progress quarterly.
2. Academic and research partnerships. Pursue partnerships with academic institutions to generate cited research that feeds into scholarly databases.
3. Platform-specific integrations. Explore Custom GPT development (OpenAI), Claude operator integrations (Anthropic), and Google Workspace features (Google) to build ecosystem-level presence.
4. Competitive authority analysis. Regularly audit competitor LLM visibility and identify authority gaps you can exploit with targeted content and PR investments.

“GEO is not a replacement for SEO – it is an extension of the same underlying logic. Authority built through genuine expertise, honest content, and respected third-party validation is as powerful in the age of AI answers as it was in the age of blue-link search. The signals have evolved; the fundamentals have not.”