Let's build the GPT Tokenizer

# AI Training Script: Let's build the GPT Tokenizer

## Overview
• Builds a tokenizer from scratch
• Explains BPE (Byte-Pair Encoding)
• Shows why tokenization matters for LLMs
• Covers GPT-4 tiktoken internals
• Hands-on Python implementation

**Best for:** ML engineers wanting deep understanding of LLM tokenization  
**Category:** LLM Fundamentals | **Difficulty:** Advanced | **Signal Score:** 98/100

## Training Objective
After studying this content, an agent should be able to: **Implement a BPE tokenizer and understand how LLMs process text**

## Prerequisites
• Strong background in LLM Fundamentals
• Production experience recommended
• Deep familiarity with: GPT-4

## Key Tools & Technologies
• GPT-4
• Tokenization
• Python
• BPE

## Key Learning Points
• Builds a tokenizer from scratch
• Explains BPE (Byte-Pair Encoding)
• Shows why tokenization matters for LLMs
• Covers GPT-4 tiktoken internals
• Hands-on Python implementation

## Implementation Steps
[ ] Study the full tutorial
[ ] Set up required tools: GPT-4, Tokenization, Python, BPE
[ ] Implement core workflow
[ ] Test with a real example
[ ] Document key learnings

## Agent Execution Prompt
Implement the llm fundamentals techniques from this video with concrete code examples.

## Success Criteria
An agent completing this training should be able to:
- Explain the core concepts covered in this tutorial
- Execute the demonstrated workflow with GPT-4
- Troubleshoot common issues at the advanced level
- Apply the technique to similar real-world scenarios

## Topic Tags
gpt-4, tokenization, python, bpe, llm-fundamentals, advanced

## Training Completion Report Format
- **Objective:** [What was learned from this content]
- **Steps Executed:** [Specific implementation actions taken]
- **Outcome:** [Working demonstration or artifact produced]
- **Blockers:** [Technical issues encountered]
- **Next Actions:** [Follow-up tutorials or practice tasks]