What the Heck is an LLM? It’s like a Tootsie Pop
You know those Tootsie Pops? Hard candy on the outside, chewy chocolate in the middle? Large Language Models, or LLMs, work a lot like that. On the surface, they sound smooth and human. Underneath, they are all math, probability, and pattern recognition. The sweet talk is just the coating. The chewy center is pure computation.
By the end of this read, you will know what an LLM actually is, how it functions, and why leaders should care. No jargon. No hype. Just the truth about what’s under the wrapper.
Generative AI vs. LLMs: The Big Picture
Before diving in, it helps to understand where LLMs fit into the larger world of AI. Generative AI is the broad category that includes any system that creates something new: text, images, code, video, or sound. It learns patterns from massive datasets and then generates fresh content that resembles what it was trained on.
Large Language Models are one of the core engines within generative AI. They process and produce language, handling the writing, summarizing, and translating of text. They also serve as the conversational layer that most people interact with. In multimodal systems (i.e. they train on and output more than text) such as ChatGPT, Claude, or Gemini, the LLM interprets your prompt, determines the type of output you want, and coordinates with other specialized models to create images, audio, or code.
Generative AI is the candy factory. The LLM is both the friendly guide who takes your order and the production line machinery moving behind the scenes.
What an LLM Actually Is
A Large Language Model is software trained on enormous amounts of text so it can predict what word or phrase should come next. It trains on billions of text examples to learn how words and ideas tend to appear together. Over time, it builds internal representations that capture how words relate to one another in context.
When you ask a question, it does not know the answer. It estimates the probability of each possible next word and chooses one based on those patterns. It doesn’t know facts in the way humans do (though it can reproduce information embedded in its training data). That’s the chewy center: math and statistics turning into something that feels like understanding. The hard candy shell is language, the part that makes interaction feel natural.
How It Works
Think of each sentence as a long chain of tiny pieces called tokens. The model studies how those tokens usually appear together. It notices that “peanut butter” often pairs with “jelly,” or that “meeting” often follows “schedule.” During training, it adjusts its internal patterns based on the data it processes. That process is called training.
When you talk to an LLM, it does not pull an answer from a database. It uses probability to assemble “words” that fit your request. It builds sentences one step at a time, each chosen because it seems most likely to make sense given what came before. To you, it sounds like conversation. To the model, it’s math all the way down.
This pattern-building process is what makes LLMs powerful. They can summarize reports, draft content, or spot relationships in text that would take a human much longer to find. The result feels fluent because the outer layer—the words—is what we recognize and trust.
Why Leaders Should Care
You do not need to understand every technical detail to use LLMs effectively. You do need to understand their limits and strengths.
Strengths:
- They handle repetitive text tasks at scale.
- They can speed up research, brainstorming, and early drafting.
- They identify trends in large amounts of language data faster than any team could.
Limits:
- They do not reason or judge.
- They can make confident statements that are false.
- They reflect bias from their training data and even from the human feedback used to fine-tune them.
For business leaders, that means LLMs are not strategy engines. They are accelerators. The real value appears when human insight guides the machine’s pattern-spotting power. Use them to extend your team’s capacity, not to replace your team’s thinking.
The Catch
The biggest misconception about LLMs is that they understand meaning. They don’t. They simulate understanding by generating patterns that resemble meaning (though sometimes those patterns produce surprisingly coherent reasoning). Sometimes the simulation goes wrong. The model can “hallucinate,” inventing details that never existed. It does this because it’s built to be convincing, not correct.
Another risk is bias. Years ago, Amazon built an AI recruiting tool that prioritized male candidates because the decades of data it trained on reflected the historically male-dominated engineering field. If the data model learned from reflects human bias, and it always does, then it will reflect those same distortions. Leaders need to decide how to review, verify, and apply what these systems generate. The opportunity is huge, but only when paired with strong human oversight.
So What’s in the Middle of the Tootsie Pop?
Peel back the friendly language layer and you find the chewy core: math, probability, and patterns. That center is what gives LLMs their predictive power. The outer shell makes them approachable. Together they form a tool that feels almost human, even though it’s entirely statistical.
As you enjoy the sweet layer of AI’s conversational language, remember what is underneath. The coating charms you. The math drives it. Don’t confuse conversation with comprehension.
Next up in this series, “What the Heck Is Prompt Engineering?” Spoiler: It has nothing to do with engineering as you normally think of it.
