Had a chance to put in words what I mean by
“LLM is a grammar book on steroids” - have fun
The Language Rules Book: Why Developers Must Stop Humanizing LLMs
From the grain to the tree
Language begins as structure. The smallest grains are morphemes — the roots, prefixes, and suffixes that carry meaning.
Morphemes combine into words, words into phrases, phrases into sentences, and sentences into paragraphs.
Each level builds upon the one below, compressing more information into broader, more abstract forms.
A book, for example, distills millions of words into a single conceptual whole.
This hierarchy — from the grain to the tree — is what gives language its architecture and logic.
Grammar as pattern
Grammar formalizes recurring relationships among those linguistic elements.
It tells us how words fit together to create meaning and coherence.
When humans learn grammar, they don’t memorize every possible sentence.
They learn a small, powerful set of principles that lets them infer an infinite number of patterns.
Grammar, in this sense, is the mathematical summary of linguistic regularity.
Patterns beyond human language
The same concept applies to any symbolic system.
A sequence of chess moves, for instance, is also a language.
Its “grammar” defines which moves can legally follow others.
A system that can recognize and generate valid move sequences has effectively learned the rules of that game — the grammar of play.
When an AI model processes chess moves, computer code, or music, it does the same thing: it detects structure and predicts what should come next.
It doesn’t understand strategy, logic, or emotion. It identifies patterns.
How LLMs “learn”
Large language models (LLMs) are mathematical engines for detecting and reproducing such patterns at scale.
They predict the next token — a fragment of a word — starting from all previous ones.
By doing so across billions of examples, they internalize statistical regularities that span from local syntax to global discourse.
They “learn” not only how words fit together but also how ideas, tones, and meanings tend to flow.
What emerges is a dense network of relationships — a vast, implicit map of how language behaves.
From “grammar book” to “language rules book”
In human education, a grammar book codifies a narrow subset of those relationships: the structural rules that ensure correctness.
For LLMs, the equivalent artifact is exponentially larger and more inclusive.
Their billions of parameters encode not just grammar but how meanings, styles, and intentions interact across contexts.
This is why “grammar book” is too small a term.
A more accurate metaphor is a language rules book — a compendium of every statistically stable relationship within language.
It is the same concept, expanded in scale and scope.
The distinction is quantitative, not qualitative.
The nature of the system hasn’t changed; only its size and resolution have.
And, crucially, no matter how vast the book becomes, books don’t think.
They record and expose rules. They do not reason, choose, or understand.
The LLM, in essence, is a book that writes back when prompted.
The developer’s misconception
Many developers fall into the trap of anthropomorphism — treating this rules book as if it were a reasoning colleague.
They attribute understanding, motivation, or intent to a statistical model that possesses none.
The result is flawed design: misplaced trust, poor validation, and systems that behave unpredictably outside their training domain.
The correct framing is simpler and more productive.
The model is a tool, not a teammate.
It offers linguistic structure at industrial scale — a reference work that can generate new text consistent with learned regularities.
Your job is to engineer the workflow around it: define constraints, validate outputs, and integrate it responsibly.
What good engineering might look like
A capable developer treats an LLM the way a researcher treats a complex source — with respect, but also with scrutiny.
Define precise tasks. Be explicit about inputs, outputs, and allowed forms.
Constrain generation. Use schema enforcement, retrieval grounding, and verifiers.
Design workflows, not prompts. Break problems into structured stages: retrieve, reason, generate, validate.
Test continuously. Evaluate on real data and monitor for distribution drift.
Own responsibility. The model predicts; you ensure accuracy, safety, and context relevance.
An engineer who understands the model as a “language rules book” builds systems that are robust by design, not by hope.
The real source of intelligence
The intelligence is not inside the model.
It resides in the process of using it correctly — in the human who reads the rules book, interprets its constraints, and applies them to new problems.
The LLM encodes the structure of how humans have used language; it does not understand what any of it means.
Meaning, in the human sense, lives in interaction, purpose, and consequence — none of which exist in weights and matrices.
Closing argument
Think of the LLM as the most comprehensive language rules book ever created: a numerical encyclopedia of how words and meanings co-occur across human communication.
It can generate, rephrase, and recombine patterns with breathtaking fluency.
But it is still a book.
Books don’t think.
They inform, they illuminate, and they enable — but they never act.
The thinking belongs to the reader.
And in this era of machine-generated language, the reader is the developer.
Sorry my “French", it’s like midnight and I was too lazy on Sunday to edit it in detail. But hope it gives some idea.
The original “grain" for curious " raw” dietary adepts:
This one is from me and not adapted 
Grammar is like a set of rules for language.
Game moves are another kind of language — probably outside our usual understanding of what “language” is — but for an LLM, that’s just a set of patterns.
Patterns can exist at multiple levels of abstraction, which basically define the general rules and indirectly determine how large a pattern is (how many tokens, characters, words, or moves are analyzed to identify a pattern).
To put that in more comprehensible terms:
Language is a set of common rules that define how elements form patterns. In human language, the primary elements are morphemes (which basically inspired the logic behind tokens) and more or less stable combinations of morphemes — words.
Words form word combinations, word combinations form sentences, sentences form paragraphs, paragraphs form sections or chapters, and those form documents, books, libraries, etc. These are what I call levels of abstraction.
There’s a general rule: the higher your level of abstraction, the more content is compressed into that abstraction — essentially, the larger the pattern.
Now, if we look at game moves, those can also be seen as a kind of language — one you’ve properly encoded into elements or tokens. The same logic applies here.
The “grammar” is a summarized or reduced version of that set of rules. Knowing those grammar rules allows you to generate more language examples — sequences of elements that follow the pattern.
When humans study grammar in school, they learn how to use language without errors. By learning a smaller set of rules, they can infer the broader patterns of elements.
When LLMs were invented, the mathematical models behind them became very good at detecting much larger patterns — far larger than humans can consciously perceive or analyze. That’s how a statistical mathematical model became capable of producing text that follows rules beyond our conscious understanding, yet still belongs to the same class as those “grammar” rules (simplified descriptions of how patterns interact).
Here, “rules” means relationships between patterns of various sizes. Human grammar operates on a small scale, while the rules an LLM works with are thousands of times larger, including relationships between elements that may carry meaning to us but are still just patterns to the machine. Only the level of abstraction and size change — not the nature of the rules.
That’s why you can still technically call them grammar rules.
Training an LLM consists of assigning mathematical weights to artificial neurons, which systematize these otherwise undetectable rules into a kind of blueprint.
This blueprint for language rules is what we usually call a grammar book in school.
The “grammar book” operated by LLMs has billions of parameters — but technically, it’s still a grammar book (just on steroids).
If you see it that way, as a developer, you start to understand its limitations. Your workflows will then evolve to compensate for those limitations.
With this mindset, you stop relying blindly on the LLM and start doing your job as a developer: understand the problem, find a solution, and design a workflow that brings you from point A to point Z while accounting for potential edge cases.
Since we operate in the domain of language, text, and meaning, good workflow examples should be drawn from psychology and linguistics — disciplines that describe how humans work with text and meaning, how they solve conceptual problems, and so on.
This is what becomes difficult for many developers: they’re reluctant to switch from a familiar technical field to one they know little about.