How AI Answers Change Over Time
AI responses aren't static. Research shows how AI platform answers evolve over weeks and months, with implications for brand monitoring.
How AI Answers Change Over Time: A Tracking Study
AI responses aren’t snapshots—they’re moving targets. The answer ChatGPT gives today may differ from the answer it gives tomorrow, next week, or next month.
Understanding how AI answers change over time has direct implications for monitoring strategy, optimization efforts, and setting realistic expectations.
The Tracking Study
A case study from Trackerly examined AI response consistency over a 1.5-month period. The researcher tested a single factual question across major AI platforms: “Who runs the website Tracking Happiness?”
This question has a clear, verifiable correct answer. The website founder, Hugo Huijer, is well-documented across multiple authoritative sources including PsychCentral. Traditional search engines consistently provide accurate information.
The question tested whether AI platforms would consistently provide the same accurate answer over time.
Platform-by-Platform Findings
The results varied by platform.
ChatGPT: Confident but Wrong
ChatGPT generated fictional names for the website’s founder. The names—like “Kees van der Velden” and “Joris Henneman”—demonstrated understanding of cultural context (Dutch-sounding names for a Dutch website) while failing on factual accuracy.
The model was confidently incorrect, and the specific incorrect answers varied between queries.
Implication: ChatGPT may hallucinate plausible-sounding information when uncertain, and these hallucinations vary over time.
Google Gemini: Forgot What It Knew
Gemini’s behavior was particularly interesting. It initially refused to answer, then correctly identified the founder for three consecutive days, then “forgot” the information entirely and returned to refusing or providing vague responses.
This pattern—knowing something, then losing that knowledge—suggests information availability fluctuates in ways that aren’t predictable.
Implication: Visibility gains on Gemini may not be permanent. Correct information today doesn’t guarantee correct information tomorrow.
Claude: Partial Success
Claude achieved accuracy twice during the tracking period, with partial success other times. When attempting to provide the name, Claude often got the first name correct while struggling with the surname.
This partial accuracy pattern—getting close but not entirely right—appeared consistently.
Implication: Claude may be more likely to attempt answers with incomplete confidence, producing partially correct results.
Perplexity: Consistent Accuracy
Perplexity “consistently provided the correct answer every single time” throughout the tracking period. It offered the most reliable and detailed responses.
This consistency aligns with Perplexity’s architecture, which emphasizes source citation and verification.
Implication: For factual accuracy that depends on retrievable information, Perplexity shows the strongest performance.
Why Answers Change
Several factors drive AI answer changes over time.
Model Updates
AI platforms periodically update their models. These updates can shift:
- Knowledge cutoffs
- Reasoning patterns
- Information prioritization
- Response tendencies
After an update, the same query may produce different results even with identical inputs.
Retrieval Changes
For RAG-enabled queries, the underlying web content changes. Search results shift. New content appears. Old content disappears or moves.
Today’s retrieval produces different source material than yesterday’s, leading to different synthesized answers.
Training Data Evolution
For foundation model knowledge, the training data cutoff matters. As models are retrained on newer data, information about your brand may enter, change, or become less prominent.
Probabilistic Sampling
Even without any external changes, language models use probabilistic sampling. Different sampling paths produce different outputs, introducing inherent variability.
Implications for Brand Visibility
If even well-documented factual questions produce variable answers, brand visibility faces even greater challenges.
Your Brand Is Less Established Than You Think
Classic movies and well-known websites have abundant training data and clear web presence. Even they show variability.
Most brands have far less established presence in AI systems. The study notes that “the more diluted or disputed your presence is in the training data, the more volatile your visibility will be.”
If your brand is mentioned inconsistently, that inconsistency may be inherent to your current authority level, not a measurement error.
No Guarantees of Consistent Mention
The Gemini pattern—knowing something, then forgetting it—challenges assumptions about visibility permanence.
Achieving visibility once doesn’t guarantee ongoing visibility. Continuous monitoring catches regression before it becomes a long-term problem.
Platform Choice Matters for Reliability
Perplexity’s consistent accuracy suggests that some platforms provide more reliable visibility for brands that have strong, retrievable web presence.
If accurate representation matters (for factual claims about your brand), platform characteristics affect outcomes.
Hallucination Risk Is Real
ChatGPT generating fictional founders demonstrates hallucination risk. If AI will confidently generate wrong names, it may confidently generate wrong information about your brand.
Monitor not just whether you’re mentioned, but whether what’s said is accurate.
Monitoring Implications
Time-based variability affects monitoring strategy.
Point-in-Time Audits Are Insufficient
A single audit showing favorable visibility doesn’t indicate ongoing visibility. The Gemini pattern shows visibility can disappear within days.
Instead: Establish continuous or regular monitoring that catches changes.
Trend Analysis Over Snapshot
Given inherent variability, trends matter more than individual results:
- Is visibility generally improving, stable, or declining?
- How does current performance compare to 30-day and 90-day averages?
- Are there concerning patterns emerging?
Trend analysis smooths noise to reveal signal.
Accuracy Monitoring
Beyond visibility, monitor accuracy:
- Is information about your brand correct when mentioned?
- Are there hallucinated claims that need correction?
- Does accuracy vary by platform?
Visibility of inaccurate information may be worse than no visibility.
Platform-Specific Cadence
Given different platform characteristics, consider platform-specific monitoring cadence:
| Platform | Variability | Suggested Cadence |
|---|---|---|
| Perplexity | Lower (for retrievable facts) | Weekly |
| Gemini | Can shift suddenly | Weekly |
| Claude | Moderate | Weekly |
| ChatGPT | Higher | Multiple times weekly |
More variable platforms need more frequent monitoring.
Optimization Implications
Understanding time-based variability affects optimization strategy.
Sustained Effort Required
Visibility isn’t achieved and maintained automatically. Ongoing optimization sustains visibility against natural drift and competitive pressure.
One-time optimization projects produce temporary gains. Sustained visibility requires sustained effort.
Strengthen Foundational Signals
To reduce variability, strengthen signals AI uses to establish your brand:
- Consistent entity information across the web
- Authoritative third-party mentions
- Clear, retrievable content answering common questions
- Wikipedia and knowledge graph presence where applicable
Stronger signals produce more consistent visibility.
Monitor After Changes
After content or entity signal changes, monitor more frequently. Changes can have delayed effects as AI systems update their indexes and models.
Don’t assume changes produced results based on immediate checks. Track over weeks.
Setting Realistic Expectations
Time-based variability means:
100% Visibility Isn’t Realistic
Even strong brands won’t appear in every AI response for every relevant query. Inherent variability means some percentage of misses is normal.
Focus on visibility rates (percentage of queries where you appear) rather than expecting perfect consistency.
Short-Term Fluctuations Are Normal
Week-to-week visibility may fluctuate without indicating fundamental changes. Don’t overreact to short-term noise.
Identify concerning patterns over longer time horizons.
Different Platforms Have Different Baselines
Achieving 80% visibility on Gemini might be realistic. Achieving 80% on ChatGPT might be exceptional. Platform characteristics set different baseline expectations.
Benchmark against platform-appropriate standards.
The Ongoing Game
AI visibility isn’t a destination—it’s an ongoing game. Answers change. Visibility fluctuates. Competitors adapt. Platforms evolve.
The brands winning this game monitor continuously, optimize consistently, and adapt to changes quickly.
Static approaches produce static results in a dynamic environment. Stay dynamic.
RivalHound provides continuous AI visibility monitoring that catches changes as they happen. Start your free trial to track your visibility over time.