AI Search at Scale: What Prompt Growth Really Means
In 2023, most enterprise SEO and content teams were still operating under the assumption that AI search was experimental, an interesting layer on top of Google, not a fundamental shift in user behavior.
That assumption is now obsolete. As of July 2025, ChatGPT handles over 2.5 billion prompts per day, more than doubling from eight months prior. Roughly 330 million of those come from U.S. users. With a weekly active user base approaching 800 million, this is no longer a fringe interface, it is a dominant surface for discovery and decision-making.
Other LLM-powered tools are scaling fast. Perplexity now serves over 200 million monthly queries, and Arc Search, Claude, Gemini, and You.com all contribute to a new pattern: people are asking models for answers directly, without ever visiting a search engine.
And it is not just trivial questions. Users are asking LLMs for:
- Product recommendations (“What’s the best CRM for a startup?”)
- Strategic guidance (“How do you price enterprise software?”)
- Technical decisions (“How does feature flagging work in Kubernetes?”)
These are the same queries that once triggered high-intent traffic through Google or Bing. Now, they are being intercepted, summarized, and answered by models, often without attribution, and almost always without visibility into who influenced the output.
This is the real shift: your audience may still be “searching,” but they are no longer navigating.
The answer is the destination.
Treat LLMs as a Marketing Channel, Not a Black Box
Most enterprise teams today treat LLM search as a visibility leak, something that is siphoning away traffic without offering attribution, analytics, or control. That instinct is not wrong, but it is incomplete.
Large language models like ChatGPT, Gemini, and Claude are not just consuming web content. They are selecting it, reframing it, and delivering it to end users as direct answers. In doing so, they have become a new kind of marketing channel, one that shapes perception without the user ever visiting your site.
This is a different kind of surface than search engines. LLMs do not rank results. They generate answers. And in that answer, they make an implicit editorial choice: what gets cited, what gets ignored, and what frames the idea.
The strategic implication is straightforward: if you want to influence how your market sees a category, a capability, or a decision, you need to influence what LLMs say about it. Visibility in this context is not measured in clicks, it is measured in presence, relevance, and inclusion.
Forward-looking teams are starting to treat LLM outputs the way they treat PR or analyst coverage. If your brand or language shows up in an AI-generated answer to a key commercial query, that is earned distribution. If it does not, you have ceded the frame to someone else.
The opportunity is not just defensive, it is expansive. LLMs surface third-party content billions of times per day. Being consistently cited, or shaping the structure of an answer, is influence at scale. It is just influence without referral traffic.
This shift does not eliminate the need for traditional SEO or content marketing. But it does mean the goal has changed. You are no longer optimizing for rank. You are optimizing for inclusion. That is a different game, and it needs a different playbook.
The LLM Visibility Stack
LLMs do not use rankings. They generate answers by drawing from massive amounts of structured and unstructured content. Some of this data comes from training sets, some from real-time retrieval systems, and some from content embedded within the prompt.
That means traditional SEO logic does not apply. There is no position to win. Instead, there are five key levers that determine whether your content is cited, paraphrased, or ignored inside a model output.
- Content Structuring
Models prioritize clarity. Pages that use consistent formatting, clear headers, semantic structure, and answer-first writing are easier for models to interpret. - LLM Monitoring
There is no native dashboard from OpenAI or Google. Teams must run prompt tests or use tools like Conductor to infer visibility. - SEO Infrastructure
LLMs still rely on crawled, structured content. Schema markup, clean sitemaps, internal links, and crawlable URLs are still prerequisites for ingestion and retrieval. - Semantic Optimization
Language models understand meaning through embeddings—mathematical representations of relationships between terms, entities, and concepts. Semantic optimization means shaping content to align with how the model associates ideas. Tools like Clearscope or Surfer help evaluate semantic coverage and conceptual gaps. - Access Path Risk
Most LLM visibility tools use scraped or simulated data. They attempt to show when your content appears in AI outputs by sending prompts to public interfaces, logging responses, and extracting citations or paraphrased inclusions. These methods help fill the analytics gap, but they are inherently fragile. When OpenAI, Google, or Anthropic changes interface behavior or blocks scraping, those pipelines break. Vendors are trying to approximate model outputs, not measure them directly. Teams must vet vendor methodologies and avoid strategies built on black-box scraping.
What Gets Pulled: Content Patterns That Perform
Prompt testing and model outputs show consistent patterns in what content LLMs pull in:
High-performing formats:
- FAQs
- Definition pages
- Comparisons
- How-to guides
- Glossaries
Formatting signals:
- Clear heading structure
- Answer-first layout
- Use of Schema.org markup
- JSON-LD fields for date, author, etc.
LLMs prefer clarity, not creativity. Structure to be understood, then styled for depth.
Hybrid Retrieval Architecture: Why Visibility Requires Multiple Signals
Most LLMs use a hybrid retrieval approach to generate answers. This means they combine multiple sources of evidence before surfacing a response:
- Dense retrieval: Semantic match based on vector embeddings
- Sparse retrieval: Keyword match using token overlap
- External APIs: Real-time search results from platforms like Bing or Google (used in ChatGPT Browse, Gemini, Perplexity)
Why this matters
To appear in LLM answers, your content needs to:
- Be semantically aligned
- Be structured and retrievable
- Be available in sources that external APIs index (like StackOverflow, GitHub, Quora, Wikipedia)
Your visibility depends on being aligned across all three layers: semantic meaning, keyword context, and retrievability.
Tooling, Access, and Platform Risk
LLM visibility platforms aim to give enterprise teams insight into where and how their content appears across model outputs. These tools simulate user prompts, log generated responses, and extract structured insights to identify inclusion, omission, or phrasing changes.
These tools do not provide direct access to the inner workings of LLMs or their training sets. But they help teams reverse-engineer patterns, identify coverage gaps, and track performance over time across prompts and platforms.
Ask your vendors:
- How do you collect your data?
- Is it compliant with provider policies?
- What platforms do you track?
- What happens when your access breaks?
Use these tools for directional signal, not precision, and validate with internal prompt testing.
How to Execute: Creating Content That Performs for LLMs and People
Visibility in LLM search is not just about being indexed. It is about being useful enough that the model selects your content when generating an answer. That requires:
Format for Dual Use
LLMs need semantic clarity. People need scannable depth. Design each page to serve both.
Choose the Right Page Type
Use formats that match prompt intent:
- FAQs
- Definitions
- Comparisons
- How-tos
- Glossaries
Apply Structured Markup
Use JSON-LD schema. Validate with Google’s Rich Results Test.
Syndicate to Known LLM Data Sources
Wikipedia, StackOverflow, Quora, GitHub, .gov/.edu sources. LLMs crawl these heavily.
Run Prompt-Based QA
Simulate prompts. Log inclusion. Identify gaps.
Vet Your Tools
Require transparency. Avoid scraping-only vendors with no recovery path.
Insight Is Not Execution
Knowing where you show up is not enough. Influence comes from action.
To move from signal to strategic advantage, you need more than visibility data, you need performance intelligence. That is where platforms like Knotch One and AgentC come in.
Knotch One is a content performance platform that measures how content performs across touchpoints and audiences. It provides a system of record for impact, engagement, and outcome.
AgentC sits on top of that platform as a cross-platform analysis and content generation engine. It pairs visibility insights with concrete recommendations and AI-powered execution, helping teams go from observation to action.
When paired with LLM visibility platforms like Conductor, these tools offer a glove-in-hand strategy: see what is surfaced, know how it performs, and act on it with confidence.
Operational Impact: Who Owns This and What Changes
LLM visibility spans SEO, content, and product marketing. Teams need:
- Clear ownership (pod, lead, or shared OKRs)
- New workflows (prompt QA, visibility reporting)
- Updated content briefs (page type, schema, structure)
Make visibility a tracked performance layer, not an afterthought.
The Strategic Endgame: What “Winning” in LLM Search Looks Like
The new goal is semantic presence:
- Being included in model answers
- Framing the default explanation
- Becoming part of the model’s memory
This creates a new kind of moat. If your content defines how the model explains your category, you own the category.
Audit Yourself: 6 Questions for Your Team
- Are we tracking LLM visibility across ChatGPT, Gemini, and Perplexity?
- Are our strategic pages machine-readable and structured?
- Are we publishing to sources LLMs ingest from?
- Do we simulate prompts and monitor inclusion?
- Who owns LLM visibility internally?
- Are our vendors compliant and transparent?