Uncover the Real Questions in Your Niche

Keywords were the foundation of traditional SEO. But in AI Search, people ask: "Which CRM is easiest to set up for a small team?"—not "best CRM software." LLMHERO Prompt Simulator runs your prompts through ChatGPT, Gemini, Claude, Perplexity, and more—so you can clearly see who shows up next to you, and which customer questions you're completely missing.

Prompt research is the foundation of AI visibility

Prompt research is the process of identifying and tracking the questions that push AI systems to compare options and recommend specific brands. It plays the same foundational role for AI visibility that keyword research plays for SEO—only the unit of measurement is different.

Instead of pages and queries, prompt research focuses on how AI systems shape and present recommendations. In AI SEO, visibility only matters when AI is evaluating a choice—weighting alternatives, applying constraints, and pointing someone to a solution. If your brand isn't present in those moments, it won't be considered in the decision.

Most prompts never reach that stage. They produce explanations, summaries, or general advice. Prompt research filters those out and focuses on middle- and bottom-of-funnel prompts: comparisons, evaluations, and "best" queries where AI weighs alternatives and recommends a solution.

This is fundamentally different from traditional keyword tracking. SEO rankings are usually relatively predictable. AI-generated answers are volatile and personalized. Prompt research focuses on direction and pattern recognition—not fixed positions or exact counts.

Why do you need a Prompt Simulator?

Because you can't optimize what you don't measure. Most brands still don't know whether AI recommends them—or ignores them. LLMHERO Prompt Simulator removes that uncertainty by focusing on the moments where AI evaluates options and recommends decisions. With LLMHERO, those decision moments become measurable signals you can track, interpret, and act on.

Prompt research vs keyword research

For SEO marketers, prompt research introduces a familiar concept with new constraints. Unlike traditional search, we don't have years of historical data for AI prompts—no direct search volume, CPC, or trend curves.

Aspect	Keyword Research (traditional SEO)	Prompt Research (AI Search)
Unit of measurement	Keywords, pages, ranking positions	Conversational prompts, brand mentions, recommendation context
Popularity data	Historical data exists (search volume, CPC, trends)	No direct prompt volume—only estimates via search proxies
Outputs	Relatively stable and predictable rankings	Volatile, personalized AI answers
Optimization focus	Optimize for clicks and SERP positions	Optimize for mentions and citations in AI answers
Success metrics	Rank position, CTR, organic traffic	Mention rate, citation frequency, sentiment, Share of Voice
Intent analysis	Intent classified as informational/commercial/transactional	Intent defined by constraints, personas, and decision context
Persona relevance	Useful for targeting, not critical for ranking	Critical—persona constraints determine whether AI recommends anything

Is keyword research still relevant?

Yes. Keyword research still plays an important supporting role because it reveals how people describe problems and what intent sits behind searches. Those signals help you decide which prompts to target. The difference is that keywords are no longer the endpoint—they're language input that gets rewritten into natural, conversational prompts.

How to work with LLMHERO Prompt Simulator

Effective prompt research isn't a one-time exercise. It's a loop: identify → simulate → track → optimize.

Define target audiences and personas

Personas determine which questions get asked. That's true for keyword research and prompt research. But in prompt research, personas also determine whether AI recommends anything at all.

That's because constraints are what push AI systems from "explain mode" into "recommendation mode." A broad question like "What is good dog food?" produces education. A constrained question like "Best limited-ingredient dog food for a dog with a sensitive stomach under 1,000 UAH/month" forces AI to compare options.

Connect your product solution to persona problems

When people ask AI to help them choose, they rarely compare feature lists. They're trying to decide whether a product fits their situation, reduces risk, and feels like a safe choice.

AI recommendations tend to mirror that behavior. Brands are suggested more often when the product clearly solves a specific pain point the buyer feels at the decision moment.

Use keyword research as language input

Keyword research validates language for prompt research by confirming how your audience naturally describes problems—not by measuring demand. Start with a seed phrase tied to a constraint—for example, "dog food ingredients" reflects how ingredient-sensitive buyers might frame the problem.

Generate decision-stage prompts using AI

Effective BOFU prompts require context. LLMs need clarity on: who's asking, what outcome they're trying to avoid, which constraints shape the decision, how the buyer naturally describes the problem, and that the question should lead to a recommendation or comparison.

In LLMHERO Prompt Simulator:

Open Prompt Generator and select your topic. LLMHERO automatically produces the most relevant 20–50 decision-stage prompts using the context you provide. Each prompt is written the way a real buyer would ask AI—without brand names, demanding a recommendation, and avoiding "educational" phrasing.

Run data collection and track visibility

Once you've built your prompt set, the final step is to simulate it through LLMHERO and see how AI responds in real time.

LLMHERO Simulation Engine:

Select your prompt group and click Report. The platform runs each prompt through the LLM assistants you choose—whether that's ChatGPT, Perplexity, or Gemini—in parallel. For each response, LLMHERO records: whether your brand is mentioned, whether it's cited, its position in recommendations, and which competitors appear alongside you.

Results appear in the dashboard. You can see:

AI Visibility Score (what % of prompts mention your brand)
Citation Rate (what % of mentions include a link to your site)
Competitive Overlap (which brands appear with you most often)

You also get a breakdown by AI platform—maybe ChatGPT recommends you in 45% of cases, while Perplexity only does so in 12%. That unlocks targeted optimization insights.

LLMHERO lets you set an automated simulation schedule—daily—or generate reports on demand whenever you need the data. Each run creates a snapshot of AI answers, building a historical record of how your brand is positioned, compared, or ignored across decision-oriented prompts. That turns AI visibility from guesswork into a measurable signal you can act on.

Data sources: where to find prompts to track

LLMHERO integrates with multiple data sources to help you identify the highest-value prompts for simulation.

Google Search Console

Connect your GSC to LLMHERO and the platform automatically finds the questions your site already ranks for. Use regex filters to isolate queries with 6+ words or those containing interrogative words (what, how, why, should).

Automatic GSC integration
Regex filters for questions
Keyword → conversational prompt conversion

Your website analytics

Connect your analytics (Google Analytics) and the platform identifies pages already receiving AI traffic. Then it generates prompts around them to track how AI recommends those pages.

GA4 / LLMHERO Analytics integration
Server log analysis (ChatGPT-User, Perplexity-User)
Automatic prompt generation for top AI-traffic pages

Internal data sources

LLMHERO lets you upload customer support chats, sales call transcripts, help docs, and other internal sources. The platform uses NLP to extract recurring questions, pain points, and constraints—and generates persona-driven prompts from them.

Upload CSV/TXT/JSON internal data
NLP extraction for questions and pain points
Automatic persona creation based on patterns

Competitor visibility gaps

LLMHERO automatically identifies prompts where competitors are mentioned but you aren't. These are your biggest growth opportunities. The platform prioritizes them by estimated reach and shows which competitors dominate.

Automatic mention gap detection
Competitive Share of Voice per prompt
Cited pages analysis for PR outreach

Ready to Get Started?

Today, launch your first prompt simulation.