Text is tokenized into discrete units, each token is assigned a numeric ID from a dictionary, and that ID is converted into a complex vector (embedding) that represents the token's meaning in mathematical form.

A token is the smallest unit of information an AI can process, analogous to coins and bills in a currency system, and every AI operation and thought has a token cost.

Tokens are not always full words—words can be broken into multiple tokens (e.g., 'unbelievable' becomes 'un', 'believe', 'able'), allowing the model to understand linguistic building blocks like prefixes, suffixes, and roots.

AI systems do not process language the way humans do—they break sentences into discrete tokens rather than reading sentences fluidly, fundamentally changing how they parse meaning.

The retriever component searches a database for tokens mathematically similar to those in the user's query (by converting them to vectors) in order to inject relevant factual information into the token budget.

An AI system has a context window (token limit) representing its short-term memory capacity, and this limit is the absolute maximum number of tokens the model can process for any single task, fundamentally constraining its capabilities.

When a conversation exceeds the context window limit, the AI begins forgetting earlier parts of the conversation, which is why long chats can go off the rails or become incoherent.

Older AI models have smaller context windows (e.g., 8,000 tokens, comparable to a small coin purse), while newer, more powerful models have much larger context windows (e.g., 128,000+ tokens, comparable to a bank vault), enabling processing of entire books.

Within a context window, every single token counts toward a total token budget for a single interaction, including the user's question, system instructions, background information retrieval, and the generated answer—all competing for the same limited allocation.

An AI query passes through multiple processing stages (tokenization, routing, planning, information retrieval, generation) in a multi-stage assembly line, and each stage consumes tokens from the shared budget.

The core function of a large language model is to predict the probability of the next token given a sequence of prior tokens, and it performs this prediction iteratively (one token at a time) to generate complete sentences, paragraphs, and answers.

Improving AI systems fundamentally comes down to spending tokens more wisely and efficiently, rather than adding more compute or parameters.

Tokens are the universal 'currency' of AI systems because every component (retrieval, transformation, generation) reads, transforms, and generates them, making the entire system one integrated economy of transactions.

AI hallucination occurs because the model runs out of factual tokens in the budget and is forced to guess at the next token, producing plausible-sounding but false information.

Hallucination can be mitigated by allocating token budget to retrieval of real factual tokens and injecting them into the context window, providing the model with higher-quality 'money' to spend rather than forcing it to guess.

Effective AI system design requires treating the system as a financial planner for tokens: minimizing waste through concise prompts, maximizing signal by including only the most valuable information, and structuring tokens clearly so the AI knows what to attend to.

Intelligence in AI systems is not about understanding words in the human sense, but rather about sophisticated management and allocation of tokens across system components and stages.

Mastering the token economy is the real secret to building progressively smarter and more capable AI systems.