Visibility in LLMs and AI Overviews

What is AI visibility?

AI visibility refers to how content appears in responses generated by Large Language Models (LLMs) and AI-powered search features like Google's AI Overviews. Unlike traditional search rankings, AI visibility depends on whether content is retrieved for grounding (RAG) or happens to match token prediction patterns.

How LLMs generate answers

Large Language Models produce responses through two distinct mechanisms:

1. Parametric knowledge: Information encoded in model weights during training
2. Grounded retrieval: Real-time fetching of external content via Retrieval-Augmented Generation (RAG)

Understanding this distinction is essential because only the grounded retrieval component is consistently influenceable through content optimisation.

Indices vs. generators

Traditional search engines and LLM-based systems operate on fundamentally different principles:

When a URL appears in an LLM response but has no visibility in traditional search, this typically reflects the non-deterministic nature of token prediction rather than a separate "AI ranking" system. LLMs are probabilistic generators, not ranked indices.

This has practical implications: appearances in LLM outputs that aren't backed by strong retrieval signals are inconsistent and unreliable for strategic planning.

The role of grounding (RAG)

The predictable component of LLM visibility is grounding—when AI systems use RAG to fetch content before generating responses.

The grounding process is an information retrieval task that relies on:

• Indexing and crawlability
• Vector search and semantic matching
• Relevance scoring

These are the same mechanisms that power traditional search. Content that performs well in grounded AI responses typically satisfies standard SEO requirements:

• Crawlable and parsable page structure
• Clear topical relevance
• Accurate, verifiable information
• Consistent entity representation

Limitations of AI visibility tracking

Tools claiming to measure "AI visibility" face significant technical constraints:

• Non-deterministic outputs: LLMs produce variable responses based on temperature settings, conversation context, and inference paths. The same prompt can yield different results.
• No query data: Unlike search engines, LLM providers do not expose prompt volumes, impressions, or click-through data for most consumer interfaces.
• Context personalisation: Responses can vary based on user context that external tools cannot fully access or replicate (for example, account state, prior chats, or personalisation features).
• Attribution uncertainty: When an LLM cites a source, verifying that the citation influenced the response (rather than being post-hoc attribution) is technically challenging.

Prompt set bias (sampling design)

Visibility scores are a function of the prompts you choose to test:

• Prompt selection: Tracking low-quality, irrelevant, or overly broad prompts produces noisy visibility breakdowns and weak strategic signals.
• Prompt intent mix: Many prompts are not "search" behaviours (for example, drafting, summarisation, or ideation). Treating all prompts as equivalent can misrepresent how often retrieval-like behaviour occurs.
• Prompt phrasing sensitivity: Small differences in wording can change whether a system grounds to web sources, retrieves different documents, or answers from parametric knowledge.

Account and environment effects

Results can change materially depending on the environment used to run prompts:

• Model and tier differences: Subscription tier and model selection can affect latency, tool availability (such as browsing), and grounding behaviour. Results are not interchangeable unless the model and settings are controlled and documented.
• Rate limits and usage caps: Consumer products may apply limits (for example, advanced reasoning modes or tool usage). Hitting limits can reduce sampling and bias observed frequencies of certain behaviours.
• Location and locale: Country, language, and regional settings can affect retrieval sources and citations. If you need country-level tracking, treat locale configuration as a requirement, not an assumption.
• Memory and session state: Some consumer accounts include memory and long-lived personalisation that can bias answers. Prompt tracking should run in fresh sessions with memory/personalisation disabled where possible, and should avoid relying on user-specific state.

These limitations mean that metrics from AI tracking tools should be interpreted cautiously. Without access to actual user queries and verified attribution paths, the data represents sampling under artificial conditions rather than real-world performance measurement.

Prompt volume estimates (why they vary)

Some tools estimate how often prompts are used. In most cases, these numbers are not first-party data from model providers:

• Data source constraints: LLM providers typically do not publish prompt-level volume metrics for external measurement.
• Panel-based approaches: Third-party estimates often rely on sampled behavioural data (for example, browser-based panels) plus statistical modelling to correct for demographic and device coverage gaps.
• Noise and filtering: Raw prompt streams include many non-commercial and non-search prompts; tools frequently filter for commercial intent terms, which can change results substantially depending on the classifier and rules used.

For technical decision-making, treat prompt volume estimates as directional at best. Because LLM usage patterns differ substantially from traditional search behaviour, prompt volumes should not be expected to correlate with Search Console or paid search data—they measure fundamentally different user intents and interaction modes.

Overlap with traditional SEO

The optimisation requirements for AI visibility overlap substantially with traditional search optimisation:

Technical foundations

• Clean crawl paths and server responses
• Proper HTTP status codes
• Structured data markup
• Fast, reliable page delivery

Content requirements

• Clear topical focus and comprehensive coverage
• Consistent entity naming and representation
• Accurate, verifiable claims with sources
• Logical information architecture

Information structure

• Parsable page layouts
• Clear hierarchies and heading structures
• Contextual internal linking
• Structured formats (tables, lists, specifications)

Practical optimisation

Entity clarity

Reinforce how your brand and products are understood:

• Consistent naming conventions across the site
• Schema.org markup for key entities
• Authoritative cross-references and citations
• Clear "About" and entity-defining pages

Content patterns for retrieval

Structure content to support chunking and retrieval:

• Concise, fact-rich summaries at section level
• Clear definitions aligned to common queries
• FAQ structures for direct question-answer matching
• Tables and lists that parse cleanly

Freshness and accuracy

• Display update dates and version information
• Maintain consistency across pages (avoid contradictory statements)
• Cite sources for factual claims
• Remove or update outdated information

Accuracy and brand risk

LLM responses can contain fabricated information ("hallucinations"). A 2025 study by the EBU and BBC found that 45% of AI assistant responses to news queries had at least one significant issue, with 20% containing major accuracy problems including hallucinated details.

For brands, this creates risk: appearing in AI responses doesn't guarantee accurate representation. A model may confidently state incorrect information about products, services, or company positions.

Mitigation approaches:

• Ensure accurate, consistent information is widely available for grounding
• Monitor AI outputs for brand mentions (with appropriate scepticism about tracking accuracy)
• Maintain strong traditional search presence for authoritative brand queries
• Consider that deterministic search results provide more reliable brand representation than probabilistic LLM outputs

Model collapse and content quality

Researchers have identified a phenomenon called model collapse: when AI models train on AI-generated content, output quality degrades over successive generations.

This occurs because models are optimised to produce statistically average, plausible outputs. Training on such outputs reinforces convergence toward mediocrity, attenuating the novel and exceptional content that maintains information diversity.

Implications for content strategy:

• Original, human-generated content retains long-term value as training data quality becomes a differentiator
• Synthetic content flooding may reduce the marginal value of additional AI-generated material
• Distinctive, expert-driven content becomes relatively more valuable as average-quality content proliferates

Key takeaways

1. Grounding is the controllable variable: RAG-based retrieval uses standard search mechanisms; optimise for these
2. LLM appearances are probabilistic: Non-grounded mentions reflect token prediction variability, not a separate ranking system
3. Measurement limitations are significant: Interpret AI tracking data cautiously given technical constraints
4. Fundamentals haven't changed: Technical accessibility, content quality, and entity clarity remain primary factors
5. Brand accuracy isn't guaranteed: LLM outputs may misrepresent brands regardless of optimisation efforts

If you're looking to improve your visibility in AI-generated results, our AI Discoverability consulting can help position your content for both traditional search and emerging AI interfaces.

Further reading

• Google's guidance on AI Overviews
  Official documentation on how AI Overviews work and content eligibility
• Understanding RAG (Retrieval-Augmented Generation)
  Google Cloud's explanation of the retrieval mechanism that powers grounded AI responses
• Model Collapse in AI systems
  Nature paper on how AI training on AI-generated content degrades output quality