Most of us who write code for a living have a pretty good intuition about how our own minds work. When we see a pattern enough times, we internalize it. We learn that a specific API call is flaky, that a particular algorithm scales poorly, or that a specific variable name is probably a typo. We build mental models of the systems we work with. We understand cause and effect. If we change a line of code, we have a strong intuition about what might break.
When we interact with a Large Language Model (LLM), it’s easy to project that same intuition onto the machine. It writes code that compiles. It explains complex concepts with apparent clarity. It even mimics our reasoning patterns. The temptation is to believe that it “understands” the world in a way that resembles our own cognition. But the gap between what an LLM learns and what a human engineer understands is vast, and understanding that gap is the difference between using these tools effectively and being blindsided by their failures.
The Illusion of the Database
To understand what an LLM actually learns, we have to strip away the anthropomorphism and look at the raw mechanics. At its core, a transformer model is a mathematical function optimized to predict the next token in a sequence. That’s it. There is no ghost in the machine, no spark of consciousness hiding in the matrix multiplications.
When we train a model on a massive dataset—say, the entire public internet plus a library of code repositories—we aren’t teaching it facts. We are forcing it to compress that information into a set of parameters (weights). The model learns a high-dimensional statistical representation of the data.
Consider the word “bank.” In English, this word is ambiguous. It can refer to a financial institution, the side of a river, or tilting an airplane. A human understands this disambiguation through context and lived experience. We know what a river is because we’ve seen water flow; we know what money is because we use it.
The LLM has no such experience. It only has tokens. It learns that “bank” appears frequently near “money,” “deposit,” and “interest” in one context, and near “river,” “water,” and “flood” in another. It assigns a probability distribution to these associations. When you ask it to write a story about fishing near a bank, it calculates the likelihood of “river” following “fishing” and selects the appropriate token.
It looks like understanding, but it is actually sophisticated pattern matching based on statistical co-occurrence. The model isn’t retrieving a definition from a mental dictionary; it is navigating a probability landscape shaped by the training data.
The Embedding Space
Programmers are familiar with the concept of vector spaces. In machine learning, we map discrete tokens (words, sub-words, or code symbols) into continuous vector spaces called embeddings. In this space, semantic similarity is represented by geometric proximity.
For example, the vector for “King” minus the vector for “Man” plus the vector for “Woman” results in a vector very close to “Queen.” This mathematical property emerges purely from the statistical relationships in the text. The model has learned that these words appear in similar contexts, and it has arranged them in space accordingly.
This is powerful. It allows the model to generalize. If it has learned the structure of English sentences, it can generate sentences it has never seen before. But this generalization is strictly limited to the distribution of the training data. It cannot invent a concept that violates the statistical patterns it has learned.
What LLMs Can Learn: The Statistical Backbone
When we say an LLM “learns,” we are describing a process of convergence. The model starts with random weights. It makes a prediction, compares it to the actual next token in the training data, calculates the error (loss), and adjusts its weights via backpropagation. Over trillions of tokens, this process carves a landscape of statistical regularities.
Syntax and Grammar
One of the most robust things an LLM learns is syntax. Programming languages and natural languages both have rigid structural rules. In Python, indentation defines scope. In C++, semicolons terminate statements. In English, subject-verb agreement is mandatory.
Because syntax is highly repetitive and predictable, LLMs learn it exceptionally well. They rarely make grammatical errors or syntax errors in supported languages. This is because the local dependencies in code are extremely strong. The token “def” is almost always followed by a function name (in Python). The token “SELECT” is almost always followed by a column name (in SQL).
For a programmer, this is the model’s strongest utility. It has memorized the syntax of almost every programming language in existence. It doesn’t “know” the grammar rules in the way a linguist does; it has simply learned that certain sequences of tokens have high probability and others do not.
Common Patterns and Idioms
LLMs are excellent at reproducing common coding idioms. If you ask for a Python function to read a file, it will likely generate:
with open('file.txt', 'r') as f:
data = f.read()It generates this not because it understands file handles or resource management, but because this specific sequence of tokens appears millions of times in its training data (GitHub, Stack Overflow, documentation). It has learned the “shape” of a file reading function.
This extends to boilerplate code. Ask for a React component, and you get the `import React` statement, the function definition, the return statement, and the export. The model is incredibly good at filling in the structural gaps of standard patterns.
Translation and Style Transfer
Because the model learns high-dimensional representations of meaning, it can map concepts between different domains. This is how translation works. The model learns that the vector representation of “Hello” in English and “Hola” in Spanish occupy similar semantic spaces relative to their respective languages.
Similarly, it can adopt styles. It can rewrite a paragraph of technical documentation as a Shakespearean sonnet. It does this by isolating the statistical markers of “Shakespearean sonnet” (rhyme scheme, meter, archaic vocabulary) and applying those markers to the content tokens.
What LLMs Never Learn: The Causality Barrier
This is where the intuition of the engineer clashes with the reality of the model. We operate in a world of causes and effects. We know that if we delete a file, it is gone. We know that if we push code without testing, we might break production.
LLMs operate in a world of correlations. They know that “deleted” and “file” often appear together. They know that “broken” and “production” often appear after “pushed code.” But they do not understand the causal mechanism that links these events.
The Counterfactual Failure
True understanding requires the ability to reason about counterfactuals: “What would happen if I did X instead of Y?”
Let’s take a concrete programming example. Imagine a legacy codebase with a function that looks like this:
def calculate_total(items):
# Note: items is a list of integers
total = 0
for item in items:
total += item
return totalNow, let’s say a junior developer commits a change:
def calculate_total(items):
total = 0
for item in items:
total =+ item # Typo: =+ instead of +=
return totalA human engineer looks at this and immediately understands the error. We know that `total =+ item` is equivalent to `total = (+item)`, which simply reassigns the total to the current item on every iteration. We understand the *cause* of the bug (the typo) and the *effect* (incorrect calculation).
If you feed this code to an LLM without running it, it might or might not catch the bug. It depends entirely on whether the specific pattern of `=+` being a bug appears frequently enough in the training data.
However, the LLM has never executed the code. It has no internal simulation of the CPU registers or the memory stack. It doesn’t “feel” the logic breaking. It is simply checking if the sequence of tokens `total =+ item` fits the statistical pattern of “correct code” or “common error.”
If the training data contained a million examples of `=+` being a typo, the model learns to flag it. If the training data is sparse on this specific error, the model will hallucinate that it is valid syntax.
The Limits of Generalization
Consider a scenario where you are refactoring a monolithic application into microservices. You need to extract a specific module, change its communication protocol from direct function calls to HTTP REST APIs, and handle network latency.
An LLM can write the boilerplate for the HTTP server. It can write the client code. It can even suggest libraries for serialization. But it cannot reason about the systemic implications of the change.
It doesn’t understand that network calls are orders of magnitude slower than local function calls. It has read texts *about* latency, so it might output the word “latency,” but it doesn’t have an internal model of time. It cannot predict that moving this specific loop across the network boundary will cause a 10x performance degradation.
It generalizes based on semantic similarity, not physical or computational reality. To the LLM, a database query and a local array lookup are just different tokens. It doesn’t inherently know that one involves disk I/O and the other involves RAM access.
Implicit Knowledge and Common Sense
LLMs struggle profoundly with implicit knowledge—the vast ocean of facts that humans take for granted but are rarely written down.
For example, if I ask an LLM to generate a configuration file for a server located in New York, and I ask it to set the timezone to “Pacific/Auckland,” it will likely do so without complaint. It doesn’t know that New York is in the Eastern Time Zone and Auckland is in New Zealand. It just knows that “Pacific/Auckland” is a valid string for a timezone field.
Human engineers possess a “world model.” We know that servers are physical objects located in specific places. We know that electricity is required. We know that if a server is on fire, you shouldn’t try to debug it via SSH.
LLMs have no world model. They are text predictors, not environment simulators. This is why they struggle with tasks that require grounding in reality, such as planning a physical action or debugging a hardware issue.
The Training Data Bottleneck
Another critical limitation is that LLMs are trapped in the past. Their knowledge is frozen at the moment the training dataset was compiled.
If a new programming language is invented tomorrow—let’s call it “Zap”—and it becomes the standard for the next decade, an LLM trained today will know nothing about it. It cannot infer the syntax of Zap from first principles. It cannot reason about memory safety in Zap. It has to be retrained on a massive dataset of Zap code.
This contrasts with a human engineer. A human can learn a new language by reading the specification and applying existing knowledge of computer science concepts. We can map concepts we already know (types, loops, functions) to the new syntax.
The LLM cannot do this extrapolation. It relies on interpolation within its existing vector space. It might guess that “Zap” looks like “Rust” or “Go” because those are the closest neighbors in its training data, but it cannot truly learn the language without seeing examples.
The “Stochastic Parrot” Problem
There is a concept in AI research called “stochastic parroting.” This suggests that LLMs are simply stitching together pieces of text they have seen before without any understanding of the underlying meaning.
While this is a bit reductive—it’s clear that models develop some internal representations of concepts—it highlights a fundamental limitation: originality.
LLMs cannot generate truly novel scientific theories or mathematical proofs that rely on leaps of intuition not present in the training data. They can remix existing ideas. They can combine concepts A and B in a way that hasn’t been explicitly written down, provided the statistical path between them is strong enough.
But they cannot have a genuine “Eureka!” moment where they discover a new law of physics. They are bound by the distribution of human knowledge contained in their training set.
Code Generation: A Case Study in Limits
Let’s bring this back to the programmer’s desk. We use tools like GitHub Copilot or ChatGPT to write code. Why do they work well sometimes and fail spectacularly others?
When LLMs Shine: The “Average” Case
LLMs are masters of the average. Most code written in the world is boilerplate. It’s CRUD operations, standard algorithms, basic UI components, and glue code. This code is repetitive, well-documented, and abundant in the training data.
When you ask an LLM to write a function to sort a list or fetch data from an API, you are asking it to reproduce the most probable sequence of tokens for that request. It succeeds because the solution is statistically “normal.”
It’s like asking an autocomplete to finish the sentence “The capital of France is…”. The model doesn’t need to know geography; it just needs to know that “Paris” follows that phrase with high probability in its dataset.
When LLMs Fail: The Edge Case and the Novel
Problems arise when you step off the beaten path.
Imagine you are working on a high-frequency trading system. You need to optimize a critical loop for cache locality. This requires a deep understanding of CPU architecture, memory hierarchy, and the specific instructions of the target processor.
If you ask an LLM to optimize this loop, it might suggest using a faster algorithm (like QuickSort instead of Bubble Sort) because that’s a common optimization pattern. However, it is unlikely to suggest restructuring the data layout to fit into L1 cache lines. Why? Because that specific optimization is highly context-dependent and rarely appears in general-purpose code repositories. It’s a niche, expert domain.
The LLM lacks the “deep” understanding of the hardware. It knows that “cache” is related to “performance,” but it doesn’t have a causal model of how the CPU fetches data from memory.
Furthermore, LLMs are notoriously bad at debugging complex, multi-file issues. If a bug arises from an interaction between three different services, an LLM might look at each file in isolation and declare them “correct.” It struggles to maintain the state of the entire system in its context window and reason about the emergent behavior.
Reasoning as Pattern Matching
One of the most debated topics in AI right now is “Chain of Thought” prompting. This is where you ask the model to “think step by step” before giving the final answer.
When an LLM writes out its reasoning steps, it often solves problems it would otherwise fail. For example, it might struggle with a complex arithmetic problem if asked for the answer directly. But if asked to show its work, it often gets it right.
Does this mean the model is reasoning? Not exactly. It is generating text that looks like the reasoning process of a human.
Think of it this way: The model has read millions of math textbooks. It knows the structure of a mathematical proof. It knows that “Step 1: Add the numbers” is followed by “Step 2: Multiply the result.” By generating these tokens sequentially, it offloads the computational burden onto the context window.
It is simulating reasoning, not performing it. The underlying mechanism is still just token prediction. However, this simulation is often sufficient for practical purposes. It’s a clever hack that uses the model’s strength (mimicking patterns) to compensate for its weakness (lack of internal calculation).
But this simulation breaks down when the steps require external knowledge or true causal inference. If the problem requires a step that isn’t represented in the training data, the simulation collapses.
The Danger of Hallucination
The statistical nature of LLMs leads to a phenomenon known as hallucination. This occurs when the model generates information that is plausible but factually incorrect.
Because the model is predicting the next token based on probability, it doesn’t have a concept of “truth.” It has a concept of “plausibility.” If the most probable next token is a lie, it will generate the lie.
For a programmer, this is critical. If an LLM suggests a library function that doesn’t exist, or an API endpoint that was deprecated three years ago, it can waste hours of debugging time. The code might look syntactically correct, but it references a reality that isn’t there.
This isn’t malice or deception. It’s a mathematical artifact of the training process. The model is simply blending the statistical distributions of the concepts you asked for, and sometimes those distributions overlap in a way that produces fiction.
The Future: Augmentation, Not Replacement
Understanding these limitations shouldn’t diminish the utility of LLMs. It should contextualize it. We are not dealing with a synthetic brain; we are dealing with a hyper-advanced autocomplete.
The most effective engineers using these tools today are those who treat them as powerful assistants rather than oracles. They use LLMs to generate boilerplate, to write tests, to refactor syntax, and to brainstorm ideas. But they maintain the “human in the loop” for verification, architectural decisions, and debugging complex systems.
We are the ones with the causal model of the world. We understand that deleting a file removes it from the disk. We understand that network calls can fail. We understand the business logic and the user experience.
The LLM provides the raw material—the code, the text, the patterns. We provide the judgment, the context, and the understanding of how things actually work.
Building Better Systems
As developers, we can design our systems to accommodate the strengths and weaknesses of LLMs.
For instance, rather than asking an LLM to design a complex database schema from scratch, we can ask it to write SQL queries for a schema we have designed. We provide the structure (the constraints, the relationships), and the model fills in the implementation details.
Or, in code review, we can use LLMs to spot syntax errors and style violations, freeing up human reviewers to focus on architectural issues and logic flaws—areas where the LLM is blind.
Conclusion
LLMs are a triumph of engineering and statistics. They have learned the surface patterns of human language and code to a degree that is startling. They can translate languages, write poetry, and generate functional code. But they have not learned the underlying mechanics of the world. They do not understand cause and effect, they cannot reason about counterfactuals, and they are limited by the data they were trained on.
For the curious learner and the seasoned engineer, the key is to appreciate the tool for what it is: a mirror reflecting the vast ocean of human text, capable of remixing it in useful ways, but lacking the spark of genuine understanding. By respecting these boundaries, we can harness their power without falling victim to their illusions.
