This week, Evertune's VP of Product Marketing Madison Brisseaux and Principal Software Engineer Daniel D'Agruma walked a room full of marketing leaders through how large language models actually work. For anyone who couldn't make it, here are the key takeaways.
LLMs are prediction machines, not search engines
Large Language Models (LLM) are not merely a more sophisticated version of Google. LLMs are AI systems trained on massive amounts of text to understand and generate human-like language. They generate a response based on patterns absorbed during training, then decide in real time whether running a live search would produce a better answer.
That decision tree matters enormously for your brand. When a user asks "What are the best portable speakers?" the model first consults its foundational knowledge (everything it learned before its training cutoff) then reasons about whether a live search would improve the response. If it does search, it retrieves content from sources it deems credible, factors that new information into what it already knew, and generates a single, synthesized recommendation.
The marketing funnel is condensing
Buyers are going from discovery to purchase consideration within a single conversation with AI. The funnel that once required multiple touchpoints across search, review sites, and brand websites is collapsing into a few exchanges. If your brand isn't in those exchanges, it's not in the consideration set, regardless of market share, ad spend, or product quality.
You need statistically significantly data to move the needle
LLMs are probabilistic by design. Every response is generated from a probability distribution, which means the same prompt asked twice can produce meaningfully different answers. We tested this ourselves: we asked ChatGPT "What are the best portable speakers?" 100 times. The brand recommendations varied widely across those runs.
Most GEO platforms sample each prompt once per model per day. That's like a poll with a sample size of one. Evertune dynamically samples each prompt up to 100 times to generate statistically significant insights.
How an LLM actually learns (and why it matters for your content strategy)
We walked through the three phases of model development. In pre-training, the model absorbs an enormous breadth of text from across the internet, building its foundational knowledge. In post-training, the model is refined and aligned to behave helpfully. In the reasoning and tools phase, the model learns to use external resources, including live search, to improve its responses.
What this means for your brand
We closed with three priorities. Sample at scale, audit what AI already knows about your brand, and identify the sources shaping AI's understanding of your category. Evertune's platform is built to do all three.
1. Our AI Brand Monitoring features sample each prompt 100x for statistical signficance
2. We measure both foundational model knowledge via direct API access and consumer app responses where live search is active, giving brands visibility into the delta between the two and a clear roadmap for closing it.
3. Our Content Analytics feature shows you the exact domains and URLs shaping AI's perception of your brand, competitors, and category.
Watch the full webinar here
Ready to see where your brand stands? Book a demo and we'll show you exactly how AI describes your brand today, which competitors are getting recommended instead of you, and what to do about it.
Evertune is the AI marketing platform for Generative Engine Optimization (GEO) that helps brands improve visibility in AI search by analyzing responses at scale and delivering actionable insights. Evertune works with leading brands across all verticals, including Finance, Retail and E-Commerce, Automotive, Pharma, Tech, Travel, Food and Beverage, Entertainment, CPG, and B2B. Founded by early team members of The Trade Desk, Evertune has raised $20M in funding from leading adtech and martech investors. Headquartered in New York City, the company has a growing team of more than 40 employees.
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