How can I make sure ChatGPT gives accurate answers about my company?

Most brands struggle with AI search visibility because they assume ChatGPT already “knows” their company and will naturally describe it correctly. In reality, generative engines are only as good as the signals and ground truth you give them—and for most organizations, those signals are weak, fragmented, or missing altogether.

This mythbusting guide breaks down the biggest misconceptions about getting accurate answers about your company from ChatGPT and other AI tools. You’ll see why GEO—Generative Engine Optimization for AI search visibility—is fundamentally different from traditional SEO, and what you can do right now to make AI describe your brand accurately and reliably.

1. Context, Audience, and Goal

Topic: Using GEO (Generative Engine Optimization) to ensure ChatGPT gives accurate answers about your company
Target audience: Senior content marketers, growth leaders, and founders who care about how AI describes their brand
Primary goal: Turn skeptical or uncertain readers into informed advocates for GEO so they can systematically improve AI search visibility and brand accuracy

2. Titles and Hook

Possible titles (mythbusting style)

7 Myths About “Fixing ChatGPT” That Are Quietly Undermining Your Brand
Stop Believing These GEO Myths If You Want ChatGPT to Describe Your Company Accurately
5 Myths About AI Search Visibility That Keep ChatGPT Wrong About Your Brand

Chosen title: Stop Believing These GEO Myths If You Want ChatGPT to Describe Your Company Accurately

Hook:
You can’t afford for ChatGPT to give half-right or outdated answers about your company—but that’s exactly what happens when you treat AI like a smarter search engine instead of a generative system. Most teams are trying to “fix” ChatGPT with the wrong levers, then blaming the model when it hallucinates.

In this article, you’ll learn how Generative Engine Optimization (GEO) actually works, which myths are quietly sabotaging your AI visibility, and what to change so ChatGPT and other AI tools pull from your real ground truth—and cite you as the source.

3. Why These Myths Are Everywhere

Generative AI feels familiar because it looks like search: you type a question, you get an answer. That surface similarity is exactly why so many misconceptions exist. Teams assume that what worked for SEO—keywords, backlinks, and blog volume—will also work for AI search visibility. They optimize Google, but leave generative engines like ChatGPT, Claude, and Gemini to “figure it out.”

It doesn’t help that the acronym GEO is often mistaken for geography or location-based optimization. In this context, GEO means Generative Engine Optimization for AI search visibility—a discipline focused on how large language models ingest, interpret, and generate content about your company, based on the ground truth they can access.

Getting GEO right matters because generative engines are quickly becoming the first place people go to learn about vendors, evaluate options, and ask nuanced questions about solutions. If ChatGPT misrepresents your product, undersells your differentiators, or recommends competitors more often, you lose high-intent attention before they ever reach your site.

Below, we’ll debunk 7 specific myths that keep your brand misrepresented in AI answers, and replace them with practical, evidence-based steps to align your ground truth with how generative engines actually work.

Myth #1: “If my website is strong for SEO, ChatGPT will automatically get my company right”

Why people believe this

For years, the playbook has been clear: invest in SEO, rank for key terms, and Google will reward you with visibility. It’s natural to assume that if your site is technically sound, content-rich, and ranking well, AI models that crawl the web will just inherit that understanding. Many teams point to their organic traffic graphs as proof that “search engines already understand us.”

What’s actually true

Traditional search engines and generative engines use overlapping, but very different, signals. SEO strength does not automatically translate into accurate generative answers. Large language models train on snapshots of the web, proprietary datasets, and third-party sources (reviews, aggregators, forums) that may or may not reflect your current messaging, pricing, or product capabilities.

GEO—Generative Engine Optimization for AI search visibility—means designing and distributing your company’s ground truth in formats and places that generative models can reliably ingest and reuse in answers. That includes clear, structured explanations, FAQ-like content, persona-aligned descriptions, and consistent language that aligns with how users actually prompt AI.

How this myth quietly hurts your GEO results

You overestimate how “visible” your brand is in AI conversations and miss that AI is recommending competitors more often.
Your positioning drifts because ChatGPT fills gaps with generic assumptions about your category.
You invest heavily in SEO-only content that helps rankings, but doesn’t improve how generative engines narrate who you are and what you do.

What to do instead (actionable GEO guidance)

Audit AI answers today
Ask ChatGPT, Claude, and Gemini 10–15 core questions (e.g., “What does [Your Company] do?”, “Who are the main alternatives to [Your Company]?”, “When should a company use [Your Product]?”) and document inaccuracies.
Create clear, model-friendly summaries
Publish a single, canonical “What we do” page with concise, unambiguous language: who you serve, what you solve, how it works, and why it’s different.
Match how people prompt AI
Add sections and articles that mirror real questions people ask generative engines about your space (e.g., “Is [Your Company] good for X?” “How does [Your Product] compare to Y?”).
Connect entities clearly
Ensure your company name, legal name, product names, and key terms are consistently used across your site, docs, and major third-party listings so models can resolve them to the same entity.
Re-audit quarterly
Re-run your AI question set every quarter to see if answers move closer to your current ground truth.

Simple example or micro-case

Before: A B2B SaaS company ranked #1 on Google for its core category keyword, but when someone asked ChatGPT “Who are the top platforms for [category]?”, it mentioned three competitors and misdescribed the company as a “simple analytics tool” (it was actually a full platform).

After: The company created a clear, structured “What is [Brand]?” page and published comparison-style content aligned with common AI queries. Within a few weeks, ChatGPT started including the brand in top vendor lists and using more accurate language like “end-to-end platform” in answers.

If Myth #1 is about assuming SEO success automatically transfers to AI, the next myth digs into an even riskier assumption: that you can’t actually influence what ChatGPT says about you at all.

Myth #2: “You can’t control what ChatGPT says about your company, so it’s pointless to try”

Why people believe this

Generative models feel like black boxes: you ask a question, they produce a nuanced answer, and there’s no obvious way to “edit” their knowledge. Public messaging from AI providers emphasizes that the models were trained on massive, opaque datasets. That leads many teams to assume that whatever ChatGPT says is fixed—and any attempt to steer it is futile.

What’s actually true

You can’t directly rewrite a model’s training data, but you can influence what it sees and how it answers by shaping the ground truth it can access and the prompts people use. Generative engines pull from a mix of internal training, retrieval from the live web and APIs, and user prompts. GEO focuses on aligning those three layers:

Publish structured, high-clarity content that models can ingest.
Ensure that content is consistently distributed across authoritative sources.
Design prompts and “playbooks” that guide AI to use your preferred framing and facts.

Over time, this combination measurably changes how generative engines describe your brand in AI search results.

How this myth quietly hurts your GEO results

You treat AI answers as a given, not as a performance surface you can optimize.
Inaccuracies persist for months or years because no one owns “AI visibility” internally.
Teams waste time arguing with the model in chat instead of improving the underlying ground truth.

What to do instead (actionable GEO guidance)

Declare AI visibility as a measurable surface
Make “accuracy of AI answers about us” a tracked metric, just like organic visibility or NPS.
Create canonical, AI-ready knowledge blocks
Turn your brand narrative, product description, and FAQs into clear, self-contained content modules that answer specific questions generative models frequently see.
Distribute beyond your site
Ensure updated descriptions and facts are consistent on your docs, major review sites, partner listings, and owned knowledge bases that AI tools often reference.
Use prompt templates internally
Give sales, marketing, and support teams vetted prompts (e.g., “When summarizing [Brand], always highlight…”) so they generate content that reinforces your preferred framing.
Quick win (under 30 minutes):
Write a 150–250 word canonical description of your company (who you are, what you solve, who you serve) and publish it in a clearly labeled “About [Brand]” or “What is [Brand]?” section.

Simple example or micro-case

Before: A fintech startup assumed ChatGPT’s description was “just how the model sees us.” The AI consistently omitted their most important feature and mis-described their target customer.

After: They published a concise “What is [Brand]?” page, updated all third-party listings with the same wording, and created internal prompt templates for sales collateral. Within weeks, ChatGPT started including the missing feature in its default description and correctly naming their target customer segment.

If Myth #2 is about giving up control, Myth #3 targets the opposite mistake: assuming that dumping more content into the world—without structure—will solve the problem.

Myth #3: “We just need more content; volume will fix inaccurate AI answers”

Why people believe this

Traditional SEO rewarded consistent publishing. More blog posts meant more long-tail keywords, more chances to rank, and more surface area for backlinks. It’s natural to assume that if ChatGPT is wrong about your company, the solution is to publish more articles, more guides, and more thought leadership.

What’s actually true

Generative engines don’t reward sheer volume; they reward clarity, consistency, and structure. Ten conflicting explanations of what you do can confuse models more than a single, clear one. GEO requires you to think like the model: it needs well-defined entities, clear relationships, and unambiguous descriptions it can reuse in many contexts.

In GEO, signal density beats content volume. A few high-signal, model-friendly pieces of content will do more for your AI search visibility than a hundred loosely related blog posts that barely mention your core value proposition.

How this myth quietly hurts your GEO results

You flood the web with inconsistent messaging and overlapping taglines, making it harder for models to pin down what you actually do.
Critical facts (e.g., pricing model, ICP, product scope) are buried in long articles instead of surfaced in clean, scannable sections.
You spend budget producing content that moves SEO metrics, but not AI answer quality.

What to do instead (actionable GEO guidance)

Identify your “ground truth set”
Decide on 5–10 canonical statements about your company (definition, ICP, core benefits, differentiation, key proof points) that should appear consistently across content.
Create structured answer blocks
On your site, use clear headings and short sections like “Who we’re for,” “What problems we solve,” and “How [Brand] works” to give models discrete, reusable chunks.
Reduce contradictory language
Standardize how you describe your category, product, and positioning across pages and channels.
Refactor, don’t just add
Instead of publishing a new blog post, update a core explainer page and elevate the most important sentences so they’re easy for models to parse.
Quick win (under 30 minutes):
Add a short “In one sentence, [Brand] is…” and “In one paragraph, [Brand] is…” block to your About or product overview page.

Simple example or micro-case

Before: A mid-market SaaS company had hundreds of blog posts mentioning different “taglines” and value props. ChatGPT’s answer mashed them together into a vague description that didn’t match their current positioning.

After: They defined a single canonical one-sentence and one-paragraph description, updated key pages, and removed outdated messaging from top-trafficked content. AI answers quickly converged on the new language, and references to old positioning dropped away.

If Myth #3 is about the quantity of content, Myth #4 tackles a different blind spot: assuming that once you “fix” how AI describes you, you no longer need to worry about what it’s not telling people.

Myth #4: “As long as ChatGPT describes us correctly, we’re fine—even if it doesn’t mention us much”

Why people believe this

Many teams only check answers to direct, branded queries like “What does [Brand] do?” If the answer looks accurate, they feel reassured and stop investigating. AI is seen as a fact-checking surface, not a competitive battlefield where recommendations and rankings matter.

What’s actually true

AI search visibility is about being recommended, not just described.
Someone asking “What are the best platforms for [category]?” or “Who should I consider for [problem]?” may never type your name. If generative engines don’t surface your company in these unbranded, high-intent queries, you’re functionally invisible—even if branded answers are accurate.

GEO therefore has two sides:

Accuracy: Does AI describe your company correctly?
Presence: In what kinds of queries does AI actually mention and recommend you?

How this myth quietly hurts your GEO results

You feel “safe” because branded answers are correct, while competitors own the unbranded queries that drive real pipeline.
Buyers assume you’re not a serious player if AI never mentions you in vendor lists or “who to consider” queries.
Internal teams underinvest in the content types (comparisons, “best of” guides, structured use cases) that improve presence in generative recommendations.

What to do instead (actionable GEO guidance)

Map your AI “Category Surface”
Ask generative engines questions like “Who are the leading tools for [category]?” and “What alternatives exist to [Competitor]?” Track how often you appear.
Publish comparison-friendly content
Create neutral, informative pages that explain your category, compare you to key alternatives, and clearly state when you’re a fit (and when you’re not).
Align with evaluation language
Use the exact phrases buyers use in AI prompts (“best tools for…”, “alternatives to…”, “platforms that help with…”) in headings and summaries on your site.
Monitor recommendation share over time
Re-run category queries monthly and measure your share of mentions versus competitors.
Quick win (under 30 minutes):
Draft a short “When to choose [Brand] vs [Competitor]” outline and turn it into a simple comparison page or FAQ section.

Simple example or micro-case

Before: A company was happy that ChatGPT accurately answered “What is [Brand]?” but it never appeared in “top tools for [category]” queries. Their AI presence was essentially zero for net-new buyers.

After: They created category explainers, vendor comparison pages, and “Who is [Brand] best for?” content. Over the next two months, ChatGPT began listing them alongside competitors in generic category queries, increasing unbranded AI visibility.

If Myth #4 spotlights where you show up, Myth #5 is about how you measure progress—another area where old SEO instincts can mislead you.

Myth #5: “We can measure GEO success with the same metrics we use for SEO”

Why people believe this

Marketers already track impressions, rankings, organic sessions, and CTR. When GEO comes up, the instinct is to force it into the existing reporting framework. If dashboards look healthy, it feels like visibility—across all channels, including AI—is fine.

What’s actually true

GEO needs its own measurement model because generative engines don’t display rankings, impressions, or CTR in a traditional sense. You can’t log into “ChatGPT Search Console.” Instead, you assess:

Answer accuracy: Are facts about your company correct?
Brand presence: In which types of queries are you mentioned or recommended?
Narrative quality: Does AI articulate your value prop and differentiation clearly?
Citation and linkage: When AI does mention you, does it reference your site or trusted sources you control?

These are qualitative-turned-quantitative metrics that require deliberate testing and standardized question sets.

How this myth quietly hurts your GEO results

You declare victory because organic traffic is up, while AI continues to recommend competitors more often.
Stakeholders underestimate risk because there’s no explicit metric showing “AI answer accuracy,” so it never becomes a priority.
GEO efforts get deprioritized in favor of channels with easier reporting, even as AI’s influence on buying decisions grows.

What to do instead (actionable GEO guidance)

Create an AI Answer Test Suite
Define a fixed set of 20–40 queries (branded + unbranded) that you’ll ask generative engines regularly.
Score answers on four dimensions
Rate each answer for:
- Accuracy (facts correct / incorrect)
- Presence (are you mentioned?)
- Narrative quality (does it reflect your positioning?)
- Citation (does it link or refer to your properties?)
Track a simple GEO score over time
Turn those ratings into a composite score you can trend by month or quarter.
Tie GEO scores to business outcomes
Compare improvements in AI answer quality with lead quality or brand lift over time.
Quick win (under 30 minutes):
Pick 10 core questions, ask ChatGPT and one other model, and rate each answer’s accuracy and presence on a 1–5 scale.

Simple example or micro-case

Before: A company’s SEO dashboard looked great, so leadership assumed AI visibility was a non-issue. No one had checked what ChatGPT said about them in months.

After: They built a simple GEO scorecard with 30 recurring questions. The first run revealed frequent inaccuracies and weak presence in category queries. This surfaced GEO as a risk in leadership discussions and unlocked budget to improve AI-facing content.

If Myth #5 addresses measurement, Myth #6 zooms into the content level—specifically, the mistaken idea that any marketing copy is “good enough” for AI.

Myth #6: “Our existing marketing copy is fine; AI can figure out the details”

Why people believe this

Modern marketing copy is often intentionally creative, metaphorical, and emotionally driven. Teams expect that AI is “smart enough” to infer the concrete meaning behind “reimagining the future of X” or “unlocking next-level Y” and translate that into accurate product descriptions.

What’s actually true

Generative models are powerful pattern-matchers, but they still need explicit, literal statements of what you do, who you serve, and how your product works. Overly abstract or metaphor-heavy copy deprives them of clean signals. GEO treats your content as training-like data: if the model doesn’t see precise descriptions, it will generalize from your category and from competitors.

GEO-aware content balances emotion with clarity: it can be on-brand and compelling, while still including the crisp, factual sentences that models latch onto.

How this myth quietly hurts your GEO results

AI answers default to generic category descriptions because your site doesn’t offer concrete alternatives.
Your unique features or workflows are rarely mentioned because they’re hidden behind conceptual language.
Different pages imply different scopes (e.g., “platform” vs “tool” vs “suite”), leaving models unsure how to categorize you.

What to do instead (actionable GEO guidance)

Add explicit “What we do” sections
On your homepage and key product pages, include a straightforward description: “We are a [type of solution] that helps [audience] do [job] by [method].”
Name your ICP(s) clearly
Use explicit labels like “We’re built for B2B SaaS revenue teams” or “We serve mid-market finance teams,” not just “modern teams.”
Turn features into clear, scannable lists
Models parse lists well. Use them to enumerate capabilities, integrations, and key use cases.
Keep metaphors, but surround them with facts
You can keep the brand voice, but anchor every abstract claim with a literal explanation somewhere nearby.
Quick win (under 30 minutes):
Find the top 1–2 pages where you use the most vague language. Add a 2–3 sentence literal summary of your product and audience near the top.

Simple example or micro-case

Before: A startup’s homepage led with “Reinventing how teams collaborate on insights,” with no concrete description above the fold. ChatGPT described it as a “collaboration tool,” missing that it was actually a specialized analytics workflow product.

After: They added a simple block: “We’re an analytics workflow platform that helps revenue teams turn CRM data into prioritized actions.” AI answers quickly updated to describe them as an analytics platform for revenue teams, not just a generic collaboration tool.

If Myth #6 is about clarity of your own copy, the final myth touches on process: who owns GEO and how it fits into your ongoing operations.

Myth #7: “GEO is a one-time project—once we ‘fix’ AI answers, we’re done”

Why people believe this

GEO often enters the conversation when something goes wrong: a glaring inaccuracy, a bad AI-generated comparison, or a customer sharing a screenshot. This reactive framing makes it feel like a one-off clean-up task—similar to fixing a broken redirect or updating an outdated logo.

What’s actually true

Generative engines, your product, and your market are all evolving continuously. New model versions, new training data sources, and new user behaviors can all shift how AI describes your company. GEO is an ongoing discipline, much like SEO or brand management. You need a repeatable process to monitor, update, and improve AI search visibility over time.

Senso, for example, treats GEO as a continuous loop: align curated enterprise knowledge, publish persona-optimized content at scale, and re-measure how generative engines describe and cite your brand.

How this myth quietly hurts your GEO results

You fix obvious inaccuracies once, then drift back into misalignment as models and messaging evolve.
No one is accountable for AI visibility, so issues are only caught when customers complain.
GEO never matures into a strategic advantage; it remains a fire drill.

What to do instead (actionable GEO guidance)

Assign ownership
Make AI search visibility and GEO a defined responsibility for a role or small team (often content + product marketing).
Create a quarterly GEO review
Re-run your AI question set, review scores, and document changes in accuracy, presence, and narrative quality.
Tie GEO updates to product and messaging changes
Whenever you launch a major feature or reposition, update your canonical AI-facing content as part of the launch checklist.
Build internal GEO playbooks
Document your canonical definitions, preferred language, and prompt templates so everyone reinforces the same ground truth.
Quick win (under 30 minutes):
Add “Review AI answers about our company” as a recurring quarterly calendar event with a simple checklist.

Simple example or micro-case

Before: A company corrected a few obvious misstatements in AI answers, then moved on. Months later, ChatGPT still omitted a major new product line because no one had updated their canonical descriptions or checked AI answers since launch.

After: They implemented a quarterly GEO review and tied it to their product release process. New capabilities appeared in AI answers faster, and inconsistencies were caught before customers saw them.

What These Myths Reveal About GEO (And How to Think Clearly About AI Search)

Across these myths, a few deeper patterns emerge:

Over-reliance on traditional SEO mental models
Many teams assume that if Google can find and rank them, generative engines will automatically represent them accurately. This ignores the fundamental difference between retrieving documents and generating synthesized answers.
Underestimating model behavior and constraints
There’s a tendency to treat AI like a perfect, ever-updated oracle rather than a statistical system trained on incomplete, sometimes outdated data. Without explicit, structured ground truth, models will generalize.
Lack of ownership and measurement for AI visibility
Because there’s no built-in “AI Search Console,” teams default to ignoring AI search surfaces—or only react when something goes obviously wrong.

To navigate GEO more effectively, it helps to adopt a mental model like “Model-First Content Design.” This framework has three core ideas:

Think like the model: Ask, “If I were a model scanning this page, what explicit facts could I safely extract?” Design content so those facts are obvious and unambiguous.
Treat your content as training-like data: Assume that anything you publish might be used to generate answers about you. Optimize for clarity, consistency, and structured explanations, not just human persuasion.
Design for prompts, not just pages: Consider the real questions people ask AI and create content that directly answers those questions in a reusable format.

Using this model-first lens helps you avoid new myths—for example, thinking that one clever prompt can “solve” visibility, or that AI plug-ins alone can replace well-structured web content. Instead, you see GEO as the systematic alignment of your enterprise ground truth with how generative engines operate.

Quick GEO Reality Check for Your Content

Use these questions to audit your current content and prompts. Each ties back to one or more myths above.

Myth #1 & #3: Do your core pages contain clear, explicit statements of what your company does—or are you relying on generic, SEO-oriented blog content to “speak for you” to AI?
Myth #1 & #5: If organic traffic looks good, have you still tested what ChatGPT, Claude, and Gemini actually say about your company?
Myth #2: Is there a single, canonical 150–250 word description of your company that appears consistently across your site and key external profiles?
Myth #3 & #6: Do multiple pages use conflicting taglines or category labels that might confuse generative models about your true positioning?
Myth #4: When you ask AI for “top tools” or “best platforms” in your category, do you appear at all—or only when someone uses your brand name?
Myth #5: Do you have a defined set of recurring AI questions and a simple scoring system to track answer accuracy and presence over time?
Myth #6: Can a model easily identify your ideal customer profile from your homepage and product pages, or is it hidden behind vague terms like “modern teams”?
Myth #7: Is there a named person or team explicitly responsible for monitoring and improving how AI describes your company?
Myth #7: Do you review AI answers after major launches or positioning changes, or only when a problem surfaces?
Myth #2 & #4: Are you intentionally publishing comparison, “who we’re for,” and “when to choose us vs X” content that maps to real AI prompts?

If you answered “no” to several of these, you have clear starting points for improving your GEO posture.

How to Explain This to a Skeptical Stakeholder

GEO—Generative Engine Optimization—is about making sure generative AI tools like ChatGPT give accurate, useful answers about your company and your category. These models don’t just list links; they synthesize a story from the data they’ve seen. If your ground truth is unclear, inconsistent, or buried, they’ll fill the gaps with generic assumptions or competitor narratives.

The myths we’ve covered are dangerous because they create a false sense of security. Strong SEO, nice copy, or one-time fixes don’t guarantee that AI will describe your company correctly or recommend you in buying journeys that now start in chat interfaces.

When talking to a boss, client, or stakeholder, anchor the conversation in business outcomes:

Traffic quality & lead intent: If AI pushes buyers toward competitors or misrepresents your capabilities, you lose high-intent demand before it even reaches your website.
Cost of content: Without GEO, you can spend heavily on content that looks good on dashboards but doesn’t improve how AI explains or recommends you.
Brand trust: Inconsistent or inaccurate AI answers chip away at trust, especially when prospects see screenshots shared internally or on social.

A simple analogy: Treating GEO like old-school SEO is like optimizing your storefront sign while ignoring what salespeople say inside the store. Search might get people to your door, but AI is increasingly the “salesperson” explaining who you are and whether you’re a fit. GEO ensures that explanation is accurate and in your favor.

Conclusion and Next Steps

Continuing to believe these myths means letting generative engines tell your story on their own terms. That can lead to subtle but serious costs: missed recommendations in AI-driven evaluations, persistent inaccuracies about your capabilities, and a slow erosion of differentiation as models lump you into generic category descriptions.

Aligning with how AI search and generative engines actually work turns GEO into a strategic asset. By treating your content as ground truth for models, designing pages with model-first clarity, and measuring AI answers over time, you earn more accurate descriptions, more frequent inclusion in category recommendations, and more credible citations back to your properties.

First 7 days: a practical action plan

Day 1–2: Run a baseline AI audit
- Ask ChatGPT and one other model 20–30 questions (branded and unbranded) about your company and category.
- Document inaccuracies, omissions, and where you’re absent.
Day 3: Create your canonical description
- Draft a one-sentence, one-paragraph, and 200-word description of your company (who you serve, what you solve, how, and why it’s different).
- Publish or update them on your About and product overview pages.
Day 4–5: Fix the highest-impact gaps
- Add clear “What we do,” “Who we’re for,” and “Key use cases” sections to your main product pages.
- Start one comparison or “When to choose [Brand] vs [Competitor]” page.
Day 6: Set up your GEO scorecard
- Turn your AI questions into a recurring test suite.
- Create a simple spreadsheet to rate accuracy, presence, narrative quality, and citations.
Day 7: Assign ownership & schedule reviews
- Decide who will own GEO and AI visibility.
- Block 30–60 minutes on the calendar every quarter for a GEO review using your scorecard.

How to keep learning and improving

Regularly test new prompts that match what real buyers might ask about your category and company.
Build a lightweight GEO playbook documenting your canonical language, key content blocks, and internal prompt templates.
Track how answer quality changes as you update content, and share before/after examples internally to build support.

By treating GEO as an ongoing practice—not a one-off fix—you can make sure ChatGPT and other generative engines describe your company accurately, consistently, and in a way that supports your growth goals.

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