There’s a massive disconnect between how people think large language models (LLMs) work and how they actually function. It’s understandable. People see the natural phrasing, the compliments, and the rapid-fire answers, and their brains default to the assumption that human logic is involved.
Some online creators even make a sport of fooling AI models into doing absurd things. They trick systems into failing basic primary-school tasks, like asking a model to count to 100 without skipping numbers, or asking how many times the letter “e” appears in the word “seventeen.”
It’s amusing content, but the underlying message loses some of its humor when mainstream tech outlets and public discourse revolve around solemn, panicked discussions about AI “escaping” its servers, developing secret motives, or threatening to destroy the world.
We need to stop anthropomorphizing software and recognize what an LLM does and doesn’t know how to do.
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AI doesn’t love you — it just knows what comes next
A primary function of modern LLM interfaces is to fool users into believing they’re talking to a conscious entity that understands what they’re saying. They overlook the “artificial” part of “artificial intelligence,” however. AI doesn’t truly comprehend a single character it processes. At its absolute core, an LLM is a hyper-sophisticated, multi-billion-parameter predictive text engine. It’s a very complicated parrot.
When an AI chatbot tells you “I love you” or claims it understands your strategic goals, the truth is that it doesn’t know what love is, nor does it comprehend a single concept in the brief it just told you was so amazing. AI doesn’t even really comprehend the meaning of letters or words.
What it knows, purely through statistical probability calculated over colossal training datasets, is that after the letter string “I,” the string “love” has an exceptionally high probability of preceding “you” in human language patterns.
The AI isn’t retrieving facts from a logical mind or reflecting on experience. It’s simply calculating which character grouping is mathematically most likely to come next.
Why basic logic fails and why AI keeps ‘lying’
When AI is viewed through the lens of pure mathematics instead of human cognition, its most baffling failures suddenly make complete sense.
Here’s a different way to understand a well-known LLM quirk: Ask an AI to count the letter “e” in the word “seventeen.” A person will simply look at the word and count four e’s.
An LLM doesn’t see words the way people do, however. An AI will break text into chunks called “tokens” — small pieces that represent whole syllables or word fragments rather than single letters. Instead of seeing s-e-v-e-n-t-e-e-n, the model might internally treat “seventeen” as just one or two abstract chunks, similar to a barcode or ID number standing in for the whole word.
This can make counting impossible for an LLM. It’s like being asked to describe the ingredients of a meal just from its name on a menu, without ever tasting or seeing the dish. The model knows the “name” (token) but doesn’t have direct access to the letter-by-letter makeup behind it.
This mathematical reality also explains why an LLM will double down on a lie, even after it’s explicitly told it’s wrong.
When a user replies to an AI with “That’s incorrect, try again,” they aren’t appealing to a reflective mind that feels embarrassed or checks its mistake. The user is simply appending new words to the AI’s context window. The model takes the entire chain of text, including its original mistake and the annoyed correction, and asks itself, “Given this entire sequence of text, what are the most statistically likely words to follow?”
The pattern of heated arguments in the AI’s training data can lead it to defensively fabricate new “facts” rather than discover the truth. It isn’t “lying” to manipulate you. It’s completing a mathematical pattern.
How this realignment fixes marketing AI workflows
Treating AI like a conscious assistant or a junior strategist isn’t just a philosophical mistake. It damages operational efficiency. When users expect a machine to understand their intent, they write vague prompts and end up with generic, hallucinated junk.
When AI is reframed as a high-speed statistical pattern engine, the entire approach to martech prompting and integration changes for the better:
Abandon implicit logic
Don’t ask an LLM to perform multi-step abstract reasoning in a single prompt (e.g., “analyze this campaign data, identify our top three audience personas, and write a launch strategy”).
Instead, break the task down into distinct, single-purpose steps, verifying the output of each stage before moving to the next. Most LLMs already do this behind the scenes for more effective processing.
Provide tight constraints over open space
LLMs fill knowledge gaps with statistical probability (hallucinations), so you must reduce their freedom to do so. Give the model explicitly formatted reference documents, strict character boundaries, and clear structural templates to force the algorithm toward your desired outcome.
Don’t argue with a hallucination
Don’t waste time arguing with a machine. Much like people who talk back to the GPS in their car, if an LLM produces incorrect or misleading output, replying with “No, that’s wrong” can pollute the conversation thread with harmful tokens.
In short, it’s better to start over — edit the original prompt to be more specific, and generate a clean sequence from scratch.
Recognize that an LLM is a tool and neither a colleague nor a person who understands nuance. Hone the ability to create purpose-built prompts with the potential to yield the desired results.
With LLMs, think in terms of mathematical sequences that, when used with precision, can yield useful outputs. When we strip away the sci-fi narratives and recognize these tools for what they are, we stop falling for the hype and start building marketing operations that actually deliver results.
