Let's Build the GPT Tokenizer

About this course

In this hands-on course, Andrej Karpathy — a founding member of OpenAI and one of the world's foremost AI educators — walks you through building a byte-pair-encoding tokenizer from the ground up. Tokenization is how large language models break down text into digestible pieces, yet it remains one of the most misunderstood components of modern AI. By the end of this course, you'll understand exactly how LLMs process language at the foundational level.

What you'll learn

• How tokenization works in large language models and why it matters for performance and cost
• The byte-pair-encoding algorithm and how to implement it step-by-step from scratch
• How to debug tokenizer behavior and understand edge cases in text encoding
• Why different LLMs use different tokenizers and how that affects their capabilities
• Practical techniques for optimizing tokenizers for specific languages or domains
• How to reason about and reduce hallucinations and errors caused by tokenization issues
• Real-world code patterns used by production AI systems

Who this is for

You're an AI enthusiast or engineer who's ready to move beyond tutorials and actually understand the internals of LLMs. This course is for anyone who wants to build, fine-tune, or deploy AI systems — not just use them.

• ML engineers and AI researchers — gain the foundational knowledge needed to optimize and debug language models in production systems
• Software developers building with LLMs — understand the constraints and quirks of tokenization so you can write better prompts and avoid hidden bugs in your applications

Prerequisites

Comfortable with Python and basic programming concepts. Familiarity with how neural networks work is helpful but not required — Karpathy explains each step clearly.

Why this matters for Indian learners

India's AI talent pool is growing fast, and companies like Flipkart, Amazon India, and early-stage AI startups across Bangalore, Delhi, and Mumbai are actively hiring engineers who understand LLM internals — not just those who know how to call APIs. Tokenization knowledge is especially valuable if you're working on multilingual AI (Hindi, Tamil, Bengali models) or adapting LLMs for Indian languages and dialects. Understanding this layer puts you ahead of most candidates and opens doors to senior engineering roles and research positions.

Frequently asked questions

Is this course really free?

Yes, completely free. You can watch the full course on YouTube with no paywalls or hidden charges.

How long will it take to complete?

The course is about 2 hours. We'd suggest setting aside a focused weekend afternoon or breaking it into two 1-hour sessions during the week. Pause often to code along — that's where the real learning happens.

Will I get a certificate?

This course doesn't offer a formal certificate, but you'll get something more valuable: the ability to explain and build tokenizers from scratch. That knowledge speaks louder on interviews and in real work.