How to Control Your Brand Narrative in LLMs (ChatGPT, Gemini & Perplexity)

Run a quick test right now: open ChatGPT or Perplexity and type "What does [your company] do?" The response may describe a product you retired two years ago, or compare you to competitors using criteria that no longer apply. 

That is not a hypothetical. It is a common outcome for brands that have not actively managed their LLM signals.

According to the 2026 Buyer Behavior Report, nearly 85% of buyers now turn to AI-powered search tools more frequently than Google.  

Instead of clicking through multiple websites, users now ask questions like:

• What is [brand]?
• Is [brand] good for [use case]?
• What are the alternatives to [brand]?

These prompts now shape vendor shortlists, often before a buyer has visited your website once.

The challenge is that LLMs generate answers by synthesizing information from across the internet. If the signals they learn from are outdated, inconsistent, or incorrect, they may present a misrepresented version of your brand narrative.

For companies, the first step is ensuring that LLMs consistently represent the right brand positioning, features, differentiation, and reputation. 

Here we are going to discuss topics including how LLMs construct brand narratives, what makes each platform different, and how you can systematically influence them.

Why Do LLMs Get Your Brand Narrative Wrong?

LLMs synthesize a wide range of signals from across the web. When those signals are inconsistent, outdated, or skewed, the generated response reflects that.

1. Outdated information across pages

Older descriptions of your product or company may still exist on your own website, on third-party directories, or across aggregator sites. LLMs weigh what appears most consistently across sources. That product page from 2021 may still be shaping how AI describes your core offering today.

2. Competitor comparison framing

Comparison articles and review platforms frame your product relative to competitors. These framings can become the dominant narrative in LLM responses. Particularly when they originate from high-authority domains. 

For example, repeated external mentions that describe your product as "better for smaller teams" may follow your brand for years, even after you move upmarket.

3. Mixed or negative review sentiment

Customer reviews across platforms contribute directly to how AI models summarize your product's reputation. A cluster of negative reviews about a problem you resolved a year ago can still surface as a current weakness. 

4. Community discussions

Forums like Reddit and industry discussion boards are heavily indexed and regularly cited. When a thread from 18 months ago describes your pricing as confusing or your onboarding as slow, LLMs can absorb that framing without any context about when it was written or whether it still applies.

5. Training data cutoffs

For training-based LLMs like ChatGPT's base mode (where no web search is triggered), there is a knowledge cutoff. A fixed point in time after which new information is not incorporated until the model is retrained. This is the reason the problem persists even after brands update their website.

If you repositioned your company, restructured your pricing, or released a major new capability after that cutoff, the model does not know and continues generating responses based on the version of your brand that existed when it last learned. That gap can persist for months, until the next training cycle incorporates current signals.

The distinction between RAG-based and training-based LLMs is crucial here. One responds to source changes relatively quickly. The other requires structural, long-term signal-building. This is covered in detail in the next section.

Why Does Your Strategy Depend on the Platform?

LLMs fall into two fundamentally different architectures, and the correction strategy for each is meaningfully different.

1. RAG-Based Systems (Perplexity, Gemini with Search, ChatGPT with Browsing)

RAG stands for Retrieval-Augmented Generation. These systems pull live web results at query time and synthesize them in real time.

Key characteristics:

• Responses are grounded in current web content, not a frozen training dataset
• Citations are surfaced, so you can trace exactly which sources are influencing the answer
• Influencing these platforms is closer to real-time SEO, fix the source, and the response can shift relatively quickly
• High-ranking, well-structured pages carry direct influence on what the model generates

2. Training-Based Systems (ChatGPT Base Model)

These systems learn from large datasets with a defined knowledge cutoff. They generate responses based on patterns absorbed during training, not live retrieval.

Key characteristics:

• Responses reflect what the model absorbed during training and not current web content
• Even if you update a source today, it may not affect responses until the next training cycle
• Strategy here is longer-term and structural, consistent signals and authoritative coverage over time
• The model cannot check whether your current product page says something different from what it learned a year ago

A common mistake  brands invest in updating their website and expect immediate LLM changes. That only works for RAG-based systems. For training-based models, structural authority-building over time is the lever. Both require different timelines and different types of effort.

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A complete brand narrative strategy addresses both layers simultaneously. 

Short-term: fix and strengthen the sources that RAG-based systems are actively pulling from. 

Long-term: build the consistent, authoritative signal base that training datasets will absorb in future cycles. Brands that only do one miss the other half of the problem.

Most of what shapes your LLM narrative is the broader ecosystem of sources that reference you, and that is where the real correction work happens.

Scale Theory builds visibility systems that help B2B SaaS brands control how they appear across both Google and AI search. 

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How to Control Your Brand Narrative in LLMs

Controlling your brand narrative in LLMs requires managing the signals that these models rely on. It is an ongoing process of auditing, correcting, and reinforcing.

A quick note before diving into the layers: consistent brand descriptions across your website and, where relevant, structured markup such as Organization and FAQ schema can marginally help LLMs parse your brand more cleanly. 

These are small supporting tactics, not a strategy on their own. The following framework is where the real leverage sits.

This work typically involves three layers:

1. Control brand positioning signals
2. Control brand reputation signals
3. Influence third-party sources you don't own

Each layer targets different signals, different source types, and requires different corrective actions.

Layer 1: Control Brand Positioning Signals

Positioning is about what your product is and does, your category, your ICP, your primary use cases, your key differentiators.

LLMs need to understand these clearly and consistently. When they don't, responses may describe you in the wrong category, attribute features you don't have, or omit the differentiation that matters most to your buyers.

Step 1: Track Positioning Prompts

Run these prompts across ChatGPT, Gemini, and Perplexity, responses can vary significantly by platform:

• What is [brand]?
• Give me an overview of [brand].
• What does [brand] do?

Document the responses in full. Note which platform generates which framing.

Step 2: Identify Narrative Gaps

Look for specific issues in the responses:

• Incorrect category definitions (described as a tool rather than a platform, or in the wrong vertical)
• Missing use cases that are central to your ICP
• Wrong differentiation framing, credited with strengths you don't emphasize, or missing ones you do
• Outdated features, retired products, or pricing structures that no longer apply

Step 3: Trace and prioritize source citations

Not all sources carry equal weight, so before jumping into fixes, prioritize based on impact on narrative and ease of control:

1. Owned Properties (Your Website First): Prioritize pages within your own website. These are the fastest to update, fully under your control, and often serve as primary reference points for AI systems.
2. Most Frequently Cited Sources Across Your Tests: Next pick up the pages that appear repeatedly across all your evaluation runs. These are the dominant narrative drivers, and correcting them delivers the highest immediate impact.
3. Pages Most Commonly Cited by Perplexity: The next step is to prioritize the sources Perplexity references most often. These are actively shaping AI-generated answers and should be treated as high-priority corrections.
4. Controlled Third-Party Profiles and Directories: Next, focus on platforms you can influence directly, such as G2, Capterra, or similar listings. While external, these are still manageable and frequently cited in comparisons.
5. Independent Third-Party Websites: Finally, address high-ranking external content shaping your category narrative. These may take longer to update, requiring outreach, partnerships, or content contributions, so they come last despite their influence.

Focus on fixing the highest-impact, most controllable sources first. This ensures faster narrative correction while building momentum for harder-to-influence third-party updates.

"Brands are 6.5 times more likely to be cited in AI search results through third-party sources than through their own website content."  

Ahrefs and Semrush AI Visibility Index

Step 4: Correct the narrative

Once sources are prioritized, the next step is systematic correction based on impact:

• Start by reviewing each page in order of priority and identifying information that is outdated, inconsistent, or factually incorrect. The goal is to align every high-impact source with your current positioning.
• Corrections should not be limited to body content alone. Update titles, meta descriptions, headers, internal links, and structured elements to reflect the latest positioning. Every visible and non-visible element contributes to how both users and AI systems interpret your brand.

The key is consistency and sequencing. Fix the highest-priority pages first, and ensure every correction reinforces a single, clear narrative across all surfaces.

Layer 2: Control brand reputation signals

Positioning is what your product is. Reputation is how people feel about it.

These are not the same audit and require different prompts, surface different signals, and call for different corrective actions. 

"Buyers now receive synthesized, conversational answers that shape brand perceptions long before they talk to sales. This shift is especially critical in B2B, where buyers are already deep into their decision-making journey before any outreach begins. These new behaviors are disrupting traditional funnels." 

 Andrew Cross, Co-CEO, Walker Sands

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LLMs summarize how people perceive your product based on patterns they find across reviews, comparisons, and community discussions. The question this layer answers: what is the LLM telling buyers about your trustworthiness, satisfaction levels, and how you compare to alternatives?

Step 1: Track reputation prompts

Run prompts like:

•  Is [brand] good for [category or use case]?
• What are the pros and cons of [brand]?
• What are the alternatives to [brand]?
• How does [brand] compare to [competitor]?

These questions surface how LLMs are characterizing your trust level, product satisfaction, and competitive standing.

Step 2: Identify narrative themes

Look for patterns across responses:

• Frequently mentioned pros: are these still accurate and relevant?
• Commonly cited weaknesses: are these still valid, or are they describing a solved problem?
• Competitor comparisons: are they fair, and do they reflect your current differentiation?

Some themes may be outdated. Others may be accurate and point to real product or communication gaps worth addressing separately.

Step 3: Trace the source signals

Identify where those narratives originate. Common sources include:

• Review platforms: G2, Capterra, Trustpilot, Product Hunt
• Comparison blogs and "alternatives to" articles
• Community discussions on Reddit, LinkedIn, and industry Slack groups
• Analyst articles and media coverage

Step 4: Improve Reputation Signals

Address inaccurate or outdated narratives by:

• Updating comparison pages to reflect current differentiation and product capabilities
• Publishing case studies and customer testimonials that reinforce your actual strengths
• Responding to reviews  particularly older ones that may be anchoring current LLM summaries
• Correcting factual inaccuracies in community threads particularly reddit if the thread is not archived

As the weight of accurate, current signals grows, it reshapes how LLMs summarize your brand over successive interactions.

Layer 3: Influencing Third-Party Sources You Don't Own

This is the hardest part of the problem and the area most guides skip entirely.

Third-party sources often carry more weight with LLMs than your own website. But you do not control them. You cannot edit the content. You have to earn the update  and sometimes, you have to accept that you cannot get one and build your strategy accordingly.

1. How to prioritize outreach

Not every third-party source is worth pursuing. Focus on:

• Sources actively cited in Perplexity responses confirmed active and highest priority
• Pages ranking on page one of Google for brand-related queries, brand comparisons, or alternatives searches
• Comparison articles with significant backlink profiles in your category

Start with the sources probably influencing current AI responses before spending any time on lower-cited pages. The highest-impact corrections come from the smallest number of sources.

2. The outreach process

Approaching correction requests as a PR pitch almost always fails. Frame it as a factual accuracy issue instead:

• Reference the specific claim that is outdated or incorrect
• Provide the accurate, current information with a supporting source link
• Keep the request short and specific editors and authors respond to clear, low-effort correction requests

If there is no response within two weeks, escalate. Try a different contact at the publication. Find the author on LinkedIn. If the article has a comments section, a polite, factual correction in public can shift the narrative even when the article is not updated.

3. What to Do When a Source Won't Update

You cannot always fix a third-party source. 

When outreach fails, the strategy shifts: out-signal it.

Publish stronger, more authoritative content on the same topic, more accurate, more comprehensive, better structured, and distributed to earn third-party citation. 

Over time, well-structured content on high-authority domains displaces lower-quality sources as the primary citation reference in RAG-based systems. The goal is to make it less relevant than the content you create.

Creating Content That Fills Narrative Gaps

If a gap exists because no one has published good content on a topic, you can fill it yourself. We are not gaming the system, but ensuring that accurate, useful information exists to be cited.

1. Formats that LLMs cite frequently:

• Comparison pages that fairly represent your product against specific alternatives  structured, specific, and up to date
• Structured FAQs that directly answer the prompts your buyers are running in AI tools
• Blog content addressing specific use cases or buyer questions with clear, declarative answers

Content published on your own domain and distributed to earn third-party links and mentions becomes part of the signal base that future training datasets absorb. Done well, it also earns citations directly, making it a short-term and long-term asset simultaneously.

2. Measuring Whether It's Working

Without measurement, correction efforts become guesswork  and there is no way to know whether to keep going, change approach, or declare the work done.

3. Establish a Baseline

Before making any changes, run a standard set of prompts across ChatGPT, Gemini, and Perplexity. Document the full responses, screenshot them or copy them into a tracking document. This is our benchmark.

Include both positioning prompts (What is [brand]? What does [brand] do?) and reputation prompts (What are the pros and cons of [brand]? Is [brand] good for [use case]?). The distinction matters because positioning and reputation can shift independently.

4. Set a Re-Test Cadence

Re-run the same prompts on a bi-weekly basis. Responses can shift for several reasons:

• Changes you have made to source content, which RAG-based systems pick up relatively quickly
• New third-party coverage or earned media that enters the citation pool
• LLM platform updates, model changes, or shifts in retrieval weighting

Bi-weekly is a reasonable cadence for most brands. Fast-moving categories, or brands actively working through a correction plan, may benefit from weekly tracking during intensive correction periods.

5. What to Look For

When comparing benchmark responses to current ones, track:

• Changes in category definition or product description language
• Shifts in which strengths or weaknesses are mentioned  and which are no longer appearing
• New or removed competitor comparisons
• Whether cited sources in Perplexity have changed  this is the most direct signal that your source corrections are taking effect

6. What Positive Progress Looks Like

The early indicators of improvement are: 

• More consistent language across platforms when describing your category and use cases
• Outdated weaknesses no longer appearing in pros and cons responses
• Accurate competitor comparisons replacing outdated framings
• Cited sources in Perplexity shifting toward pages you have updated or influenced

The end statement should be: The narrative LLM generates about our brand matches the narrative we intend buyers to receive.

FAQ

1. What is brand narrative control in LLMs?

Brand narrative control in LLMs refers to the process of auditing and improving the signals that AI tools like ChatGPT, Gemini, and Perplexity use when generating descriptions of your brand. 

It involves identifying inaccurate or outdated sources, correcting them where possible, and building stronger authoritative signals over time, so that AI-generated responses accurately reflect your positioning, differentiation, and reputation. It is an ongoing process, not a one-time fix.

2. How long does it take to change what an LLM says about my brand?

It depends on the platform. For RAG-based systems like Perplexity and Gemini with Search, improving the underlying source content can produce response changes within weeks, depending on crawl frequency. 

For training-based systems like ChatGPT's base model, changes may not appear until the next training cycle, which can take months. A complete strategy addresses both timelines simultaneously.

3. Which sources have the most influence on LLM brand narratives?

Third-party sources carry significant weight. According to data from Ahrefs and the Semrush AI Visibility Index, brands are 6.5 times more likely to be cited in AI search results through third-party sources, review platforms, analyst articles, comparison blogs, than through their own website content. Pages explicitly cited in Perplexity responses are confirmed active sources and should be the first correction priority.

4. Do I need to update my website to influence LLM responses?

Updating your website is necessary but not sufficient. Your own pages contribute to the signal base, and consistent, well-structured product descriptions help LLMs parse your offering more accurately. Structured markup such as Organization and FAQ schema provides marginal additional clarity. 

However, given the relative weight third-party sources carry, website updates alone rarely produce meaningful changes to LLM narratives. Third-party correction work needs to run in parallel.

5. How do I track whether my brand narrative is improving?

Establish a baseline before making any changes, document full responses from ChatGPT, Gemini, and Perplexity across a standard set of positioning and reputation prompts. 

Re-run those same prompts monthly and track changes in how your brand is described, which sources are cited, and which competitive comparisons appear. Consistent improvement in language accuracy, source quality, and narrative alignment across platforms are the signals to watch for.

6. What should I do if a third-party source is spreading incorrect information about my brand?

Start with a direct, factual correction request to the publisher. Reference the specific inaccuracy and provide accurate, sourced information, frame it as a factual issue, not a PR request. If outreach fails, shift to an out-signaling strategy: publish stronger, more authoritative content on the same topic so that RAG-based systems have better sources to cite. Over time, well-structured and frequently-cited content displaces lower-quality sources in AI-generated responses.

7. How is controlling LLM brand narratives different from traditional SEO?

Both require high-quality content, authoritative third-party mentions, and clear page structure. But the objective differs. Traditional SEO targets rankings for specific queries. LLM narrative control targets how AI systems synthesize and characterize your brand in response to open-ended questions buyers are asking before they run a Google search at all.