We hear about AI everywhere these days.
And yes, it stands for Artificial Intelligence — an intelligence created artificially, to help machines do tasks, that would otherwise require human intelligence. 💡
But my first question was : How AI know what I am asking or telling it?Simple answer: AI models read & understand the text we give them.
Actual answer: Tokenization, the language of AI. 🧩
So How AI Reads Text 🧠
We humans have evolved to process words almost instantly.
We no longer focus on individual letters — it all happens rapidly in the back of our minds.
Words come together to form meaningful sentences seamlessly.
AI works similarly but with a twist. When an AI model receives a prompt (a question or instruction from us), it doesn’t see it as full sentences or even just words.
Instead, it breaks the sentence down into smaller units called “Tokens” 🧱
Tokens Are NOT Word Count! 🚫
So what Are Tokens?
Tokens are the building blocks of how AI processes text.
They are not the same as words but are smaller chunks of text.
For example:
The word “Understandable” is split into 3 tokens: “Under”, “Stand”, “Able”.
The question “What is the capital of India?” is tokenized as:
“What”, “is”, “the”, “capital”, “of”, “India”, “?”.
Each word or piece of text is tokenized this way, for the AI to process and understand. 📝
IMPORTANT: Tokens and AI Costs 💰
Here’s where it gets interesting: The number of tokens matters!
AI companies charge based on the total number of tokens you send in your prompt and the AI’s response.
For example:
If your input has 50 tokens and the AI generates a 100-token response, you’ve used 150 tokens total for that interaction.
Companies like OpenAI, Amazon, and others use these token counts to determine their pricing plans. 💵
And Why Tokens Matter 🌟
Understanding tokens helps us better appreciate how AI works and why costs vary.
Every word, punctuation mark, and even a long word broken into smaller chunks adds to the token count.
AI services charge based on the number of tokens processed — both in the input (your question) and the output (AI’s response). Here’s how it works:
Short queries cost less as they use fewer tokens.
Complex or detailed responses use more tokens, increasing the cost.
For instance, if you input 200 tokens and the response is 300 tokens, the total is 500 tokens.
Each service has different pricing models. For example:
GPT-4 might charge $0.03 per 1,000 tokens, so a query with 1,500 tokens would cost $0.045.
Every token matters because, it determines the cost, context limits (how much text the model can handle at once), and even how efficiently the AI performs.
And As Everything is Happening on Cloud, lets explore it in AWS
Tokens in AWS AI/ML Services 🛠️
AWS integrates tokenization across many of its AI/ML services:
Amazon Comprehend uses tokens to process text for tasks like sentiment analysis and entity recognition.
Amazon Lex tokenizes user input to understand intents and manage conversational AI interactions.
Amazon Bedrock enables interaction with foundational models, where efficient token usage can optimize costs.
When using these services, remember: the fewer unnecessary tokens, the more cost-effective and efficient your interactions can be. 💸
So, the next time you chat with an AI model, know that tokens are silently working behind the scenes, making the magic happen. ✨
And If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.
Stay curious & motivated — keep learning & moving forward.
Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!
If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!
Now, Take a deep breathe and Go Learn🌏
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