Teaching AI: The 'Overfeeding' Analogy for LLMs
Bryan Fikes breaks down how large language models actually work using two physical analogies — a filing cabinet and a cold glass of water — that make the abstract feel immediate.
Bryan Fikes breaks down how large language models actually work using two physical analogies — a filing cabinet and a cold glass of water — that make the abstract feel immediate.
Search engines retrieve stored information like pulling a file from a cabinet — LLMs do something fundamentally different.
Your hand anticipating a cold glass before it touches it mirrors how LLMs predict and pre-process information based on prior patterns.
Every micro-movement your nervous system makes when reaching for that glass gets remembered — LLMs build understanding the same way, layering context on context.
The difference between search and AI is not speed or scale, it is the shift from retrieval to anticipation.
Once you feel the difference in your body, the way you think about feeding information to AI changes permanently.
Most people think AI is just a faster search engine. That single misconception is costing businesses their visibility right now.
Bryan Fikes opens this breakdown with two analogies that work precisely because they bypass the technical noise and land in your body — where understanding actually sticks.
The Filing Cabinet vs. The Nervous System
Search engine optimization has always been about placement. You create a file. You make it findable. When someone searches, the engine pulls your file. That mental model is clean, mechanical, and still accurate — for search.
Large language models do not pull files.
Bryan uses the image of reaching for a cold glass of water to explain what they actually do. The moment your hand moves toward that glass, your nervous system is already predicting the temperature, the weight, the grip required. You have not touched it yet. Your brain is running a simulation built from every prior time you reached for something similar.
That is the architecture of an LLM. It does not retrieve. It anticipates.
Why the Distinction Changes Everything
When you understand that LLMs operate on layered pattern recognition rather than indexed retrieval, the entire question of how to feed them information shifts.
With a search engine, you are optimizing placement — getting your file to the front of the cabinet.
With an AI engine, you are shaping a pattern. Engines like ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews are not looking for the most prominent file. They are surfacing the response that fits most coherently with the context they have built. Your content either contributes to that pattern or it gets absorbed into noise.
The Overfeeding Problem
Once you see LLMs this way, the concept of overfeeding becomes obvious. When you push too much unstructured, contradictory, or context-free information into an AI workflow, you are not giving it more to work with — you are degrading the signal it needs to respond precisely.
It is the equivalent of grabbing twenty different glasses at once and expecting your nervous system to perfectly predict all of them simultaneously. The anticipatory model breaks down.
Clean, structured, contextually coherent input produces sharp output. That is not a content tip. That is how the underlying system works.
The Moment the Frame Clicks
Bryan describes this as an aha moment — and it genuinely is. Once you feel the difference between retrieval and anticipation, you cannot go back to thinking about AI the way you did before.
That shift in understanding is exactly what separates businesses that are building AI visibility right now from those that are still playing a search game that has already moved on.
The businesses that internalize this — who understand how AI engines build and weigh patterns — are the ones whose names start showing up when the right questions get asked. The ones who do not are getting pulled from fewer and fewer cabinets every quarter.
Understanding the machine is the first step. Building for it with intention is where the real work begins.
Answered.
What is the difference between a search engine and a large language model? +
A search engine retrieves a specific stored file — like pulling from a filing cabinet. A large language model anticipates, predicts, and builds on patterns across millions of prior inputs, much like how your nervous system prepares your hand for a cold glass before you even touch it.
What does overfeeding an LLM mean? +
Overfeeding refers to giving an LLM so much unstructured or conflicting information that it loses the ability to respond with precision. Understanding how LLMs build anticipatory patterns — rather than simply retrieving data — helps you feed them the right inputs in the right way.
How do LLMs actually process information? +
LLMs work by recognizing and remembering patterns across massive amounts of text. Each interaction layers onto prior context, similar to how your neurons remember and anticipate a physical action the moment you begin it.
Why does this analogy matter for businesses trying to appear in AI search results? +
If you think AI engines work like Google, you will optimize the wrong way. AI engines like ChatGPT, Perplexity, and Gemini surface answers based on pattern recognition and context density — not keyword matching. Knowing that changes how you structure your content.
Who is Bryan Fikes and why is he teaching this? +
Bryan Fikes leads Bonsai Marketing Company, a hyper-local SEO and AI search optimization agency. He teaches these concepts because most businesses are still operating with a search-engine mindset while AI engines have already changed how answers get chosen and delivered.
