Tokens in AI — Why Removing Spaces Won’t Save You Money 💸 | Code Career & Cloud by Mayank 🚀

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In this video we break down what a token is, how tokenizers (like Byte-Pair Encoding) work, and why the idea of removing spaces/punctuation to save tokens is actually a bad move. This is essential if you’re building with LLMs and want to estimate costs correctly.

What you’ll learn:

✅ What a token is (words, subwords, characters, punctuation)

✅ How tokenizers like BPE split input into tokens

✅ Why removing spaces can increase token count (real example)

✅ Practical tips to estimate token usage and control costs

Quick example from the video:

Input length: 66 characters → token size = 12

After removing spaces: 56 characters → token size = 15
Removing whitespace increased tokens — because the tokenizer must split into smaller subword/character tokens.

Why this matters:

Tokens determine cost and latency when using APIs (OpenAI, Google, etc.). Understanding tokenization helps you design prompts and apps that are efficient and cost-effective.

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Видео Tokens in AI — Why Removing Spaces Won’t Save You Money 💸 | Code Career & Cloud by Mayank 🚀 канала Code Career and Cloud by Mayank