How to perform GEO Prompt Research

Summary

Generative Engine Optimization (GEO) requires understanding the questions users ask in search engines and large language models. This guide provides a step-by-step framework to identify, validate, and refine high-value prompts using ChatGPT, Google Search Console, and SERP features such as predictive search (autocomplete) and People Also Ask (PAA).

Introduction

As search behaviour evolves, users are increasingly asking long, conversational questions rather than typing short keywords. These questions are now being answered not only by traditional search engines (in AI Overview), but also by large language models (LLMs) such as ChatGPT, Gemini, etc. GEO focuses on making sure your brand, services, and content are surfaced in those answers, and to do so, it all starts with figuring out for which prompt you want to be surfaced. 

Effective GEO prompt research is not guesswork. It is a structured process that combines real user data, SERP analysis, and AI-assisted ideation. The goal is to identify prompts that offer business value and can realistically be optimized. Below is a repeatable framework you can use to build and refine a strong GEO prompt list for any business.

At seoplus+, we see GEO as a new pillar of our existing SEO expertise, not a replacement but an extension.

Step 1: Identify business scope 

Before generating any prompts, you need to clearly define what matters most to your business. GEO prompt research is only effective when it is tightly scoped. Skipping this step, you risk generating prompts that are technically relevant but strategically useless.

Start by identifying your core business priorities. This includes:

• Primary services or products that drive revenue
• Secondary offerings that support or upsell those core services
• Geographic scope, including countries, regions, cities, or service areas
• Any constraints such as seasonality, operational capacity, or regulatory limitations

Be intentional about what you exclude 

Not every service offered justifies a GEO investment. Focus on areas where visibility in generative answers would directly support business goals. This focus ensures your time is spent on high-return opportunities.

Conduct a high-level website audit

Before finalizing your scope, evaluate the existing website:

• Are there dedicated, optimized pages for your services? Where are the content gaps that would require creating a new page?
• Does your site structure realistically support optimization for the services and locations you are targeting?

If a prompt cannot be supported by an existing or planned page, it should not be prioritized. GEO is not just about identifying high-intent questions. It is about identifying questions you can realistically answer better than competitors.

A well-defined scope at this stage makes every subsequent step faster, more focused, and more defensible.

Step 2: Introduce your business context to ChatGPT

Once you’ve clearly defined the scope, the next step is to introduce that context to ChatGPT in a structured way. The quality of the prompts you receive is directly tied to how clearly you describe the business, its offerings, and its environment.

Start by summarizing the client as if you were briefing a colleague who has never worked on the brand before. Be concise, but specific. Vague descriptions produce generic questions that are rarely useful for GEO.

Key details to include:

• The industry or general field
• Primary services or products
• Geographic focus
• Any important differentiators identified during scoping

Once context is defined, use ChatGPT to generate an initial list of long-form questions that potential customers might ask large language models when researching, comparing, or deciding to purchase services like those offered by your business.

Example prompt:

This is [ my business]. We operate in [industry] and serve [location]. Our most important products or services are [list]. Can you give me a list of questions people could typically ask LLMs that would return answers, including companies like mine or information found on my website?

Provide 30 questions ordered by bottom of the funnel, middle of the funnel, and top of the funnel, with 10 questions per stage.

Why the funnel structure matters

GEO is not only about awareness. Many generative answers appear during evaluation and decision-making moments, especially for complex or high-consideration services.

At this stage, treat the output as a working draft, not a final output. Your goal is to:

• Surface assumptions about how users might phrase questions
• Identify early patterns across funnel stages
• Create a baseline list that can be validated and challenged using real data in later steps

Do not optimize or edit these questions yet. That comes later. For now, you are establishing a reference point that will evolve as you move through data validation and refinement.

Step 3: Identify long-form queries already driving traffic

Before expanding your GEO prompt list, it is critical to understand how users are already finding the client through long, conversational searches. These queries represent proven demand and real-world language, making them one of the strongest validation signals in GEO research.

Use Google Search Console to uncover these queries

Google Search Console is especially valuable here because it captures queries that have already resulted in impressions or clicks for your site. Unlike traditional keyword tools, it reflects actual user phrasing rather than modelled estimates.

Steps:

1. Go to Google Search Console and navigate to the Performance report
2. Click “+ New” → Query → then select Custom (regex).
3. Enter the following regex to isolate long-form queries: ^\S+(?:\s+\S+){9,}.*

This regex filters for queries with 10 words or more. The number can be adjusted depending on the size of the dataset and the industry. For some sites, 11 or 12 words may surface more nuanced questions. For others, a lower threshold may be necessary to avoid filtering out too much data.

What to look for in the filtered list

Once the regex is applied, review the remaining queries with a critical eye. Pay close attention to:

• Queries structured as full questions
• Repeated themes or patterns across different queries
• Language that reflects comparison, decision making, or problem solving

These queries often map closely to how users phrase questions to large language models, even if they were originally typed into Google.

Export and save your data

Export the filtered data as a CSV so it can be reviewed, categorized, and reused in later steps. This dataset becomes a foundational reference for validating and refining AI-generated prompt ideas.

If little or no data appears, that is still useful insight. It may indicate:

• Low visibility for long-form queries
• A content gap on the site
• An opportunity to lead in generative answers, where competitors are also likely weak

This step grounds your GEO strategy in real user behaviour rather than assumptions, ensuring that future prompt selection reflects how people actually search.

Step 4: Refine prompts using GSC data

At this stage, you are moving from hypothesis to validation.

The initial list of prompts generated by ChatGPT is based on its general understanding of user behaviour. While useful, it is still theoretical. GSC data, on the other hand, reflects how real users are already finding your site through long, conversational queries. This makes it one of the most valuable inputs for GEO research.

Upload or paste your exported GSC data into the same ChatGPT conversation and ask it to reassess the original 30 questions.

Example prompt:

This is how people found us on Google when asking long-form questions. My goal is to find the best questions to focus on in my generative engine optimization campaign. Does this change your opinion on the 30 questions you gave me earlier?

Generate a new list of questions, taking this data into consideration. If you change nothing, explain why.

What to look for in the output

This step is about identifying alignment or misalignment between AI-generated assumptions and observed search behaviour. As you review the output, focus on:

• Prompts that closely mirror how users naturally phrase questions
• Gaps between AI-generated assumptions and real search behaviour
• Opportunities where existing demand is already visible but not fully addressed on the site

If ChatGPT significantly changes its list, examine those changes critically. If it keeps most or all of the original prompts, the justification it provides is just as important as the result. A strong defence usually indicates that the original prompts already align well with observed user intent.

Step 5: Explore predictive search and People Also Ask (PAA)

After grounding your research in GSC data, the next step is to observe how Google actively guides users as they search. Predictive search and the PAAsection offer real-time insight into how questions evolve as users refine their intent.

This step is intentionally exploratory, but it still needs structure.

Before you begin, set a firm time limit of 20 to 30 minutes. Without constraints, it is easy to chase tangents that feel interesting but offer little strategic value.

Start with predictive search

Begin typing core services or product terms related to your GEO focus directly into the Google search bar. Observe how Google completes the query

Look for autocomplete suggestions that:

• shift intent
• add qualifiers (e.g., “for families,” “with spa,” “which allow pets”)
• localize the query (e.g., “in fallsview”, “near the falls”, “in Niagara falls, Canada”)
• introduce pricing or comparison language

Predictive search suggestions are based on aggregated user behaviour. When a phrase appears consistently, it is usually because a meaningful number of users are searching that way.

Review the People Also Ask (PAA) section

Next, examine the PAA section for queries related to your key services or topics. Expanding one question introduces new, related questions; start with questions that are closely aligned with your target services, then go deeper into more specific versions of earlier queries.

PAA is especially useful for identifying how users move from general curiosity to a more detailed evaluation.

As you review both features, focus on capturing questions that:

• Reflect genuine uncertainty or decision-making
• Clearly map to middle or bottom funnel intent
• Use language that feels natural and conversational
  

Avoid the temptation to collect everything. You are not building a master list of questions. You are identifying high signal prompts that either validate or challenge your existing assumptions.

Document any strong candidates and move on once your time limit is reached. The value of this step comes from focused observation, not volume.

Step 6: Create a second draft of prompts

By this stage, you have three distinct inputs: an initial AI-generated prompt list, real long-form queries from Search Console, and qualitative insights from predictive search and People Also Ask. Step 6 is where those inputs are intentionally merged.

Return to your existing ChatGPT conversation rather than starting a new one. Keeping the full context allows the model to evaluate patterns across all previous data instead of treating each input in isolation.

Add the questions you identified during predictive search and PAA exploration, and clearly explain where they came from.

Example prompt:

Here are additional questions I found using Google predictive search and the People Also Ask box. Reevaluate your previous 30 questions, taking this new data into consideration. You are allowed to keep the list unchanged, but if you do, explain your reasoning.

The goal here is not to force change. It is to pressure test the existing list against fresh qualitative signals.

As you review the revised output, assess it through a strategic lens:

• Which prompts are consistently reinforced across multiple data sources
• Which prompts feel artificially generated or overly generic
• Which questions better reflect how users naturally articulate their problems

Strong GEO prompts tend to appear in more than one place. They are echoed in Search Console data, predictive suggestions, and PAA patterns. Prompts that only exist in AI output, with no supporting signals elsewhere, should be treated cautiously.

This step often results in subtle but meaningful refinements rather than a complete overhaul. Even small wording changes can significantly improve alignment with user intent.

By the end of Step 6, you should have a second draft prompt list that is both AI-informed and behaviour-validated, making it far more defensible than the original version.

Step 7: Narrow down to the strongest prompts

After multiple rounds of refinement, you should now have a solid list of GEO prompts that are well aligned with user behaviour. The next step is to reduce that list to a manageable and high-impact set. Prioritization is essential. Trying to optimize for too many prompts at once almost always leads to diluted results.

Ask ChatGPT to assist with prioritization, but treat its response as input, not a final decision.

Example prompt:

If I were to focus on only 10 prompts to optimize for on my website, which ones would you choose and why?

This forces the model to evaluate tradeoffs rather than simply expanding the list.

Evaluate prompts against three core criteria

When reviewing the prioritized list, assess each prompt through a business-first lens.

1. Business impact.
   Prioritize prompts that are most likely to influence revenue, lead quality, or conversion behaviour. Prompts tied to high-intent decision-making generally deserve more weight than broad informational questions.
2. Strategic opportunity.
   Look for prompts where your business has a realistic chance to appear in generative answers. This includes situations where:
   • Competitor coverage is weak or inconsistent
   • Existing content can be improved rather than built from scratch
   • The business has clear expertise, authority, or differentiation

3. Execution feasibility.
   Be honest about what can actually be supported on the site. If a prompt would require extensive new content, complex approvals, or ongoing maintenance that is not feasible, it should be deprioritized.

ChatGPT’s explanations are often as valuable as the list itself. A strong rationale can highlight blind spots or confirm instincts. A weak or generic rationale is a signal to challenge the recommendation.

By the end of this step, you should have a focused set of prompts that are defensible, actionable, and aligned with both user intent and business goals.

Step 8: Validate, map, and refine prompts before execution

At this point, you should have a short list of prioritized GEO prompts. Step 8 ensures those prompts can realistically be supported on the website and aligned with broader SEO strategy before any optimization work begins. This is where prompt research turns into an execution ready plan.

Step 8a: Map prompts to themes and keywords

If the selected prompts feel strategically sound, the next step is to connect them to your existing keyword research. GEO does not replace traditional SEO. It complements it.

Provide ChatGPT with your keyword research and ask it to associate each selected prompt with the most relevant keyword or thematic cluster.

The goal is to:

• Ensure each prompt has a clear topical home on the site
• Avoid overlapping prompts that would compete with one another
• Identify whether prompts align better with existing pages or new content opportunities

This mapping process often reveals hidden issues, such as multiple prompts relying on the same keyword theme or prompts that do not clearly map to any meaningful search concept. Both are signs that refinement is needed.

Step 8b: Iterate until the prompt set is execution ready

If you are not satisfied with the selected prompts, this is the point to intervene manually.

Common reasons to revisit selection include:

• Too much overlap in intent or wording
• Misalignment with client goals or positioning
• Prompts that require content the client cannot realistically produce

Select your own version of the top prompts and ask ChatGPT to evaluate them.

Example prompt:

Here are the 10 prompts I am considering focusing on. Evaluate whether this is a strong selection for a generative engine optimization campaign and explain why or why not.

Use the feedback to stress test your reasoning, not to override it. If needed, return to Step 8a and remap prompts until the list feels balanced, distinct, and achievable. Only once your prompts are clearly mapped and strategically aligned is it worth moving into execution.

Use ChatGPT as a collaborator, not a decision maker

ChatGPT works best as a brainstorming partner. It helps surface ideas, challenge assumptions, and organize thinking. It does not have access to your full business context, resource constraints, or execution responsibilities.

Do not select prompts if:

• You have no relevant page to optimize (published or planned)
• You lack resources to create new content
• The prompt does not align with the client’s actual goals

Ultimately, GEO prompt research is a strategic decision. The final judgment should always come from the strategist who understands the client, the site, and the roadmap.

Ready to apply GEO to your own website?

Generative Engine Optimization is still evolving, and most businesses are experimenting without a clear framework. At seoplus+, we approach GEO services the same way we approach SEO services: as grounded in data as possible, aligned with business goals, and focused on execution, not hype.

If you want help identifying high-value GEO prompts, validating them against real search behaviour, and translating them into content that actually supports revenue, our team can help.

Talk to seoplus+ about building a practical GEO strategy.

A note on limitations and the future of GEO research

It is important to acknowledge the limitations of this framework.

This process exists because, today, we do not have direct visibility into how users search within large language models. There is no equivalent to Google Search Console for LLMs. We cannot see impressions, clicks, or exact prompt usage. As a result, GEO prompt research is inherently indirect.

The approach outlined in this guide is based on informed conjecture, common sense, observed user behaviour in traditional search, and the general knowledge that large language models have publicly shared about how they process and respond to questions. It is not a perfect system, but it is the most defensible one available with the data we currently have.

Until LLM providers release their own version of search performance reporting, GEO work will continue to rely on proxies such as long-form queries, SERP features, and qualitative pattern recognition. That does not make the work invalid, but it does mean that assumptions must be continuously challenged and strategies adjusted as new information emerges.

If and when LLMs introduce transparent performance data similar to Google Search Console, GEO prompt research will shift from hypothesis-driven frameworks to fully data-driven decision-making. When that happens, many of the steps in this guide will evolve or become unnecessary.

Until then, the goal is not certainty; the goal is to make the best possible decisions using the strongest signals available, while staying flexible enough to adapt as the landscape changes.

If you want to explore further how SEO and GEO are changing, and how businesses can adapt their strategies accordingly, browse our latest insights on search, generative optimization, and emerging trends.

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