Quentin Fournier
Artificial Intelligence (AI) tools like ChatGPT, Copilot, or Claude can read, write, and analyze text in ways that feel natural. But behind the scenes, they don’t see words exactly as we do—they work with something called tokens.
If you’ve ever wondered why your conversation with an AI “cuts off,” or why long documents sometimes get shortened or summarized incorrectly, the answer almost always comes back to tokens.
Understanding what tokens are will help you:
Tokens are the basic units of text that AI models use to process language. Instead of reading entire words or sentences, the model breaks text down into smaller chunks.
Think of tokens like LEGO bricks. A paragraph is a finished model, but before you build it, you need the individual pieces. AI models look at your text one token at a time, analyze the structure, and then generate new tokens to continue the response.
A helpful rule of thumb is that one token generally corresponds to ~4 characters of text for common English text. This translates to roughly ¾ of a word (so 100 tokens ~= 75 words).
📌 Example:
Different AI providers count tokens slightly differently, but the principle is the same: tokens are the currency of language for AI.
Tokens matter because they set the rules for what an AI model can handle.
Here are some concrete examples of how tokens affect day-to-day AI use:
Here are three ways to work smarter with tokens:
The shorter and more structured your prompt, the fewer tokens you use and the easier it is for the AI to stay accurate.
Instead of:
“Tell me everything you know about marketing, customer acquisition, email campaigns, and social media strategies.”
Try:
“Summarize the top 3 email marketing strategies for small businesses. Give examples.”
It’s okay to build your request step by step. For example, rather than asking the AI to summarize a 100-page report in one go, ask it to:
This way you stay inside the token window and avoid missing key details.
One of the most powerful ways to avoid hallucinations is to stop repeating yourself. Instead of typing the same long prompts over and over, you can build specialized agents that already know how to handle a specific task.
Even better: by connecting those agents to your data—whether it’s your CRM, knowledge base, or document library—you don’t need to copy and paste huge amounts of text into the prompt. You simply ask a question, and the agent will search the connected data, retrieve what’s relevant, and generate an accurate answer.
This means:
📌 Example: Instead of pasting a 50-page FAQ into a chatbot, you can build a Support Agent connected directly to your documentation. A customer asks a question, the agent fetches the right answer automatically—no hallucinations, no wasted tokens.
Curious how many tokens your text has? Use this tool:
👉 Click here to calculate your tokens
Tokens may seem technical, but they’re actually the key to understanding how AI works.
They explain:
For businesses, understanding tokens means saving money, avoiding hallucinations, and making AI outputs more reliable.
By writing focused prompts, breaking down big tasks, and connecting AI to real company data, you make sure your AI assistant isn’t just powerful—it’s dependable.
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