Understanding AI Tokens: The Building Blocks of AI Applications

AI tokens are fundamental units that AI systems use to process and generate text. Think of them as the currency in a digital conversation economy – every word has a cost, and understanding this currency helps explain why AI applications can become expensive to run.

What Are AI Tokens?

Tokens are small pieces of text that language models process. They aren’t exactly words – they’re smaller chunks that might be:

Complete words (like “hello” or “computer”)
Parts of words (like “un” and “likely” from “unlikely”)
Punctuation marks (like “.” or “?”)
Special characters

Imagine language as a puzzle. Traditional computers might see each word as a single puzzle piece, but AI models break language down further into smaller, more manageable pieces called tokens.

For instance, when you say “uncomfortable,” an AI doesn’t see just one word. It might see “un” + “comfort” + “able” – three separate tokens that together form the complete meaning. Common words like “the” or “and” might be single tokens, while specialized terms like “photosynthesis” might be broken into multiple pieces.

This tokenization is similar to how children learn to read – first understanding letters, then syllables, then complete words. AI follows a similar path, building understanding from these fundamental units.

How Tokens Impact AI Application Costs

The economics of AI applications flow directly from token usage. Consider a water utility analogy: just as you pay for each gallon of water flowing through your pipes, companies pay for each token flowing through their AI systems.

When you interact with ChatGPT or Claude, you’re essentially turning on a digital faucet. Every question you ask (input tokens) and every answer you receive (output tokens) adds to the bill. A simple question might cost pennies, but a lengthy conversation analyzing a 30-page document could cost significantly more.

Large language models are like luxury water treatment plants – they provide higher quality, but at a premium price per unit. A basic model might charge $1 per million tokens, while an advanced model might charge $30 per million tokens.

NOTE: The price of AI tokens and computing is expected to continue decreasing over time. They are already falling rapidly, driven by several factors including the release of open-source models like Llama 3.1. When API providers don’t have to recoup the cost of developing a model, they can compete directly on price, leading to ongoing price reductions.

The Power of Context: How AI Remembers Your Conversation

Context in AI is like short-term memory in humans. When you’re having a conversation with a friend, you naturally remember what was said a few minutes ago. Similarly, AI models maintain a “memory” of your interaction through what we call the context window.

Think of the context window as a moving spotlight on a long scroll of text. As new tokens enter from one end, old tokens eventually fall out the other end. This spotlight can only illuminate a certain number of tokens at once – that’s the context size.

When you ask, “What about the red one?” a human friend knows you’re referring to something previously mentioned because they remember the context. AI works the same way, but its memory is strictly limited by token count.

The context window is like a train with a fixed number of cars. New information (tokens) boards at the front while old information eventually gets dropped off at the back once the train is full. Premium AI models have longer trains – they can remember more of your conversation, but at a higher cost per mile traveled.

How Tokens and Context Flow Through AI Systems

The context window – how much information the AI can consider at once – is like the size of your water tank. Larger tanks (context windows) cost more but allow for more comprehensive processing. When someone asks Claude to analyze an entire book, they’re essentially requesting a very large water tank.

The journey of tokens through an AI system resembles water flowing through a series of connected pools. Your input text is first broken down into tokens – like raindrops falling into the first pool. These tokens then flow into the vast reservoir of the AI’s context window.

With each interaction, new tokens pour in while the oldest ones spill out once the reservoir reaches capacity. A small chatbot might have a shallow pool holding only 4,000 tokens (roughly 3,000 words), while advanced models like Claude can maintain deeper pools of 100,000+ tokens.

When you upload a large document, you’re essentially dumping a large volume of water into this system all at once. The system must process each token, which takes computational resources – like pumps and filters in our water analogy. Each token processed adds to your bill.

A concrete example: Uploading a 20-page research paper might consume 30,000 tokens. If you then have a back-and-forth conversation about this paper that adds another 10,000 tokens, you’re using 40,000 tokens total. At a rate of $10 per million tokens, this would cost about 40 cents – small for one interaction, but significant when scaled to thousands or millions of users.

How Tokens Impact AI Application Costs

Optimizing Token Usage for Cost Efficiency

Efficient token usage is similar to water conservation. Just as you wouldn’t leave the tap running unnecessarily, well-designed AI applications don’t waste tokens.

Consider how differently these prompts would be tokenized: “Tell me about artificial intelligence in detail with many examples” versus “AI overview”

The first might use 12-15 tokens, while the second uses just 2-3 tokens. Both might yield useful information, but at dramatically different costs when scaled to millions of interactions.

Smart AI developers design systems that accomplish goals with minimal token usage – like engineers designing low-flow fixtures that maintain performance while reducing consumption.

Understanding AI Inference

We explore AI inference on this blog post– the moment when an AI model actually generates a response. Think of training as building a factory, while inference is running that factory to produce goods. We examine how inference resembles a production line where raw materials (your prompts) are transformed into finished products (AI responses), and how different production schedules (batch processing vs. real-time) affect both cost and delivery speed.

As always, hope this helps!

Disclaimer: I work for Dell Technology Services as a Workforce Transformation Solutions Principal. It is my passion to help guide organizations through the current technology transition specifically as it relates to Workforce Transformation. Visit Dell Technologies site for more information. Opinions are my own and not the views of my employer.