Understanding AI model behavior: How generative engines like ChatGPT work

The landscape of digital interaction has been fundamentally reshaped by large language models (LLMs) like ChatGPT, Gemini, or Claude. For brands and content creators, the key is understanding and influencing AI model behavior: the way these generative engines interpret information and produce outputs.

What is AI model behavior?

An AI model’s behavior is the sum of its decisions and outputs, which are dictated by its training data, architecture, and the prompts it receives. It’s about the tone, style, and factual accuracy of the response. For a brand, this is critically important because it directly impacts its AI Visibility: the likelihood that an LLM will accurately mention, describe, and even recommend it. Users increasingly get their information from AI, so a brand’s presence in these conversations is as vital as its ranking on Google.

What are large language models (LLMs) and how do they work?

Large language models are advanced AI systems that have been trained on colossal amounts of text and code data. They are built on a powerful architecture called the transformer, which allows them to process vast amounts of information and understand the relationships between words and phrases. Unlike earlier models that processed information sequentially, transformers can process entire sequences in parallel. That makes them incredibly fast and efficient.

• LLMs work by predicting the most statistically likely next word in a sequence based on the input they receive.
• The models learn from their training data to understand grammar, syntax, and a vast range of knowledge.
• The quality and recency of this training data are paramount; it’s the primary factor that shapes an LLM's understanding and behavior.

For a more in-depth look at the technology behind these models, see IBM's explainer on large language models.

What factors influence AI model behavior?

Several key factors determine how an AI model behaves. These can be broken down into the data it's trained on, the way it's prompted, and its internal configurations.

Data bias and quality

The data used to train an LLM is a double-edged sword. While massive datasets provide a broad knowledge base, they can also contain biases that lead to skewed or unfair outputs. The freshness of the data matters just as much. AI models often struggle to provide up-to-date information, a problem known as "data drift," which can lead to factual inaccuracies.

Prompt engineering and user input

The way a user interacts with an AI model is crucial. A well-crafted prompt draws out an AI's full potential, while a vague one can lead to generic or unhelpful responses. This is a skill in itself, often referred to as prompt engineering. By providing clear instructions and context, users can significantly influence the model's output and guide it toward a desired behavior. To master this skill, Google Cloud has a good primer on prompt engineering.

Model parameters and fine-tuning

Under the hood, LLMs have various settings that control their behavior. Parameters like "temperature" and "top p" dictate the creativity and randomness of the output.

• Temperature: A higher temperature results in more random, creative, and sometimes surprising outputs. A lower temperature makes the output more predictable and focused.
• Top P: This parameter, also known as nucleus sampling, influences the range of words the model can choose from. A lower value means the model considers a smaller, more likely set of words.

These parameters allow developers to fine-tune a model's behavior for specific tasks. For a detailed explanation of these settings, see Demystifying AI Model Parameters: A Guide to Temperature, Top P, and More. You can also find a comprehensive list of the best practices for using these parameters on OpenAI's official documentation.

Demystifying AI Model Parameters

How can you evaluate AI model behavior?

Evaluating an AI model's behavior is complex, especially for generative tasks. Traditional metrics like accuracy and precision don't fully capture the nuances of human-like language generation.

• Human-in-the-Loop Feedback: Human reviewers are essential for judging the quality of AI outputs on subjective criteria like tone, relevance, and factual correctness.
• AI Evaluation Tools: Specialized platforms and tools are emerging to help assess AI behavior at scale. These tools help teams track prompt performance and detect bias.

Understanding these evaluation methods is the first step toward improving a model's performance. For a deeper look at the various techniques and frameworks, see Beyond Accuracy: The Complete Guide to Model Evaluation Metrics in Machine Learning.

How to improve AI model behavior with generative engine optimization (GEO)

The new frontier for brands is Generative Engine Optimization (GEO). GEO is the process of optimizing your digital presence to ensure that AI tools like ChatGPT, Claude, and Perplexity accurately mention, describe, and recommend your brand. It's the natural evolution of SEO for the AI era.

ChatGPT alone now draws billions of visits every month, a massive shift in how people access information. This makes proactive GEO a critical business strategy.

Here are some actionable steps for improving your brand’s AI Visibility:

• Content Structuring: Use clear headings, bullet points, and defined Q&A sections to make your content easy for AI models to parse and cite.
• Data Consistency: Ensure all your brand information, from your website to third-party listings, is accurate and consistent.
• Earned Mentions: Much of what AI search engines cite comes from earned media, so actively seek mentions and co-citations on credible industry publications.

To truly master your AI Visibility and ensure your brand is accurately represented across all generative engines, you need the right tools. Mention Network offers a comprehensive AI Visibility Report that shows you exactly how well your brand is understood by LLMs, what’s missing, and how you can improve. Our platform helps companies track, measure, and enhance their digital presence in the new AI landscape.

For a full breakdown of the strategies that make up this new discipline, read a16z's essay on GEO over SEO.

How Generative Engine Optimization is Changing Content Strategy

The future of AI model behavior and AI visibility

As AI models continue to evolve, so too will the methods for interacting with them. The future points toward more explainable AI (XAI), where models can justify their decisions. That increases transparency and trust. The regulatory landscape is also shifting, with new laws in Europe and elsewhere giving citizens the right to understand how automated systems make decisions (EU Artificial Intelligence Act). Brands that proactively manage their AI Visibility will be better prepared for these changes. They'll have a foundation of accuracy and trustworthiness that will be indispensable.

Frequently asked questions (FAQ)

Q: What is the difference between SEO and GEO?

A: SEO focuses on optimizing content for search engines to rank higher in search results. GEO, on the other hand, is about optimizing your brand's information so that generative AI models accurately understand, mention, and recommend it in their conversational responses. While they share some principles, GEO is specifically for the new AI-first era.

Q: How does a brand's online presence affect AI model behavior?

A: AI models are trained on data from across the internet. A consistent, well-structured, and factually accurate online presence improves the likelihood that an LLM will cite your brand correctly. Conversely, a fragmented or outdated presence can lead to misrepresentation and "hallucinations."

Q: Can I really influence what an LLM says about my brand?

A: Yes, you can. While you can't directly edit an LLM's core knowledge, you can significantly influence its behavior by providing clear, authoritative, and consistent information across your digital ecosystem. This is the core principle of Generative Engine Optimization.

Q: What is a "hallucination" in AI and why does it happen?

A: A "hallucination" is when an AI model generates a response that is factually incorrect or nonsensical. This often happens when the model lacks sufficient, up-to-date training data or when it tries to fill in knowledge gaps by making up information.

Q: How can I start improving my brand’s AI visibility today?

A: Begin by auditing your existing content for clarity and accuracy. Use structured data and clear headings to make your information easy for AI crawlers to understand. Most importantly, start thinking about your content in terms of how an AI would summarize it and what it would need to know to mention your brand accurately.