Large language models do not process text the way humans do.
While we naturally read words and sentences, llms transform all input into a very different internal
representation made up of tokens, vectors, and fixed length context windows.
This transformation is not a minor technical detail.
It explains nearly every strength and limitation of modern AI systems.
Understanding these three concepts answers some of the most common questions people have about LMS.
Why do prompts suddenly stop working?
Why does the model forget earlier parts of a long conversation?
Why do API costs scale with usage?
And why do token limits even exist?
All of these behaviors trace back to how text is represented internally.
Once you understand tokens, Embeddings and context windows LLM behavior stops feeling mysterious.
You begin to see patterns and constraints clearly.
This knowledge shifts you from being a user of AI to being an engineer who can design systems around
these constraints.
In practice, this understanding is what allows you to build reliable, scalable, and cost efficient
AI applications.
Rather than fighting against model limitations, tokens are the atomic units that large language models
actually process.
Unlike humans who perceive complete words and phrases.
Break text into smaller pieces called tokens.
These tokens can represent entire words, subwords or even individual characters, depending on the
tokenization strategy.
For example, the word unbelievable might be split into tokens, un and believable.
This splitting is intentional.
It allows models to understand word components, reuse common patterns, and handle rare or unfamiliar
words more effectively by sharing tokens across related words.
Models generalize better and require smaller vocabularies.
One of the most important points to understand is that token count is not the same as word count.
A single word may map to multiple tokens, while several short words may collapse into fewer tokens.
This difference has real world consequences.
Token counts affect pricing, context limits, and how you design prompts.
Once you think in tokens instead of words, many practical LLM behaviors become easier to predict and
control.
Tokenization strategies define how text is broken into tokens, and each strategy represents a trade
off between simplicity, flexibility, and efficiency.
The most intuitive approach is word based tokenization, where each word is treated as a single token.
While easy to understand, this method struggles with rare words, typos, and multilingual text, and
it leads to extremely large vocabularies.
Modern models instead rely on subword tokenization techniques such as byte pair encoding, wordpiece,
and unigram language modeling.
These approaches break words into meaningful chunks, balancing vocabulary size with flexibility.
They allow models to handle unseen words by combining familiar subwords.
This is why models like GPT and Bert can generalize so effectively.
Another approach is byte level tokenization, which operates directly on raw bytes.
This method is fully language agnostic and can handle any input including emojis, code, and special
characters.
The downside is that it often produces longer token sequences and less interpretable tokens.
The core trade off is clear, more flexible.
Tokenization handles edge cases better, while coarser tokenization produces cleaner, more interpretable
representations.
Modern models carefully optimize this balance once text has been converted into tokens.
The next transformation occurs.
Each token is mapped to a high dimensional vector known as an embedding.
These embeddings are not random.
They are learned during training by exposing the model to billions of text examples.
You can think of embeddings as coordinates in a large semantic space with hundreds or thousands of dimensions.
Each dimension captures some aspect of meaning, usage, or context.
For example, the embedding for king will be positioned near queen and monarch, but far from unrelated
words like bicycle.
This transformation is fundamental.
Llms do not understand words in a human sense.
They operate entirely on mathematical relationships between vectors.
All reasoning, generation, and pattern matching happens in this abstract space.
The pipeline is straightforward.
Raw text becomes tokens.
Tokens become embeddings, and embeddings flow through transformer layers.
Once you internalize this process, it becomes clear why llms excel at tasks like semantic similarity,
reasoning, and generalization.
Everything starts with embeddings.
The real power of embeddings comes from how they are organized in space in an embedding space.
Similar concepts cluster together while dissimilar concepts lie far apart.
This geometric structure enables many advanced capabilities that go beyond simple text generation.
Semantic search is a direct result of this structure.
Instead of matching keywords, systems can retrieve information based on meaning.
A query like reduce costs can automatically match documents discussing budget optimization or expense
reduction, even if the exact words differ.
Embeddings also enable clustering, allowing large collections of text such as customer feedback or
research papers, to organize themselves by topic without manual labeling.
Retrieval.
Augmented generation relies heavily on embeddings to find relevant information from large knowledge
bases and inject it into prompts.
Context matters as well.
The word Python may cluster neo, Java, and JavaScript in a programming context, or near Cobra and
reptile in a biological context.
Modern models dynamically adjust embeddings based on surrounding tokens, allowing meaning to shift
with context.
Every large language model has a context window, which is the maximum number of tokens it can process
at one time.
This limit is not arbitrary.
It is a fundamental architectural constraint that shapes how models work.
The context window includes everything the model sees system instructions, conversation history, retrieved
documents in a rag, setup, the user's current input, and even the space needed for the model's response
when the total number of tokens exceeds the window.
Older tokens are truncated and effectively forgotten.
This explains many real world behaviors.
Long conversations gradually lose early context.
Large documents must be split into smaller chunks.
Multi-step debugging sessions eventually hit a wall.
Context is not infinite memory, it is a fixed resource.
Each token in the context window consumes memory, compute, and cost.
For engineers, this means context must be managed intentionally.
Understanding what fits in the window and how information flows through it is essential for building
reliable AI systems.
Context design is just as important as prompt design.
Context window size represents one of the most important engineering trade offs in systems.
Larger context windows allow models to process longer documents, maintain conversation history, and
reason over complex inputs.
However, they also require significantly more memory, increased API costs, and slow down inference.
Smaller context windows are faster, cheaper, and more efficient, but they limit how much information
the model can consider at once.
Long conversations lose context quickly, and large documents must be carefully managed.
This trade off means that bigger is not always better.
Effective LLM applications are designed around context constraints rather than ignoring them.
Critical information should be placed where the model pays the most attention.
Unnecessary verbosity should be removed.
Every token counts.
Techniques like intelligent chunking, sliding windows with overlap.
Summarization and retrieval.
Augmented generation helps stretch limited context further.
The key takeaway is simple but powerful.
Context is a scarce, expensive resource.
Treat it like memory in traditional programming.
Allocate it intentionally.
Manage it carefully, and optimize relentlessly.