Syllabus Live

Syllabus Module 1.1

Tokenization Hub

Before an LLM can understand text, it must first break language into tokens. In this module, you will learn how raw text becomes token IDs, how tokenizer algorithms like BPE and WordPiece work, why non-English text can increase token usage, and how tokenization affects API cost, context windows, RAG pipelines, and AI agents.

Beginner FriendlyInterview FocusedProduction Relevant

What You Will Master

Explain what tokens are and why LLMs process numbers instead of raw strings.

Understand token IDs and tokenizer vocabularies mapping rules.

Compare character-level, word-level, and subword tokenization models.

Describe BPE, WordPiece, and SentencePiece merging statistics.

Analyze token inflation, API cost margins, and context window limits.

Connect token boundary counts to RAG chunking and agent memory loops.

The LLM Tokenization Pipeline

Click on any step of the horizontal sequence to see how raw text resolves into vectors.

Active step: Raw TextContext Visualizer

Human-readable text input by the user. E.g., 'I love AI'. Characters are the base representation.

State output:

"I love AI"

Learning Path Lessons

7 submodules ready

Submodule 01

What Is Tokenization?

Learn how raw text is converted into tokens and token IDs before entering an LLM.

Beginner

Outcomes you will master:

•Understand what tokens are

•Explain token IDs

•Describe the LLM input pipeline

Duration: 12 min readRead Lesson

Submodule 02

Character, Word & Subword Tokenization

Compare character-level, word-level, and subword tokenization with simple examples.

Beginner

Outcomes you will master:

•Compare tokenizer types

•Understand why subwords are used

•Identify tokenization trade-offs

Duration: 15 min readRead Lesson

Submodule 03

BPE, WordPiece & SentencePiece

Deep dive into common tokenizer algorithms used by modern NLP and LLM systems.

Intermediate

Outcomes you will master:

•Explain Byte Pair Encoding

•Understand WordPiece

•Understand SentencePiece and Unigram

Duration: 18 min readRead Lesson

Submodule 04

Token IDs, Vocabulary & Embeddings

Connect tokens to vocabulary IDs, embeddings, and the transformer input pipeline.

Beginner

Outcomes you will master:

•Explain tokenizer vocabulary

•Understand token IDs

•Connect tokens to embeddings

Duration: 14 min readRead Lesson

Submodule 05

Token Inflation, Context Window & API Cost

Learn why token count affects LLM pricing, context length, latency, and production architecture.

Intermediate

Outcomes you will master:

•Estimate token usage

•Understand token inflation

•Optimize prompts for cost

Duration: 16 min readRead Lesson

Submodule 06

Tokenization in RAG & AI Agents

Understand how tokenization affects chunking, retrieval, memory, and agent workflows.

Intermediate

Outcomes you will master:

•Design token-aware RAG chunks

•Control agent memory size

•Reduce context waste

Duration: 18 min readRead Lesson

Submodule 07

Tokenization Interview Guide

Prepare clear interview answers for tokenizer, BPE, token IDs, context window, and cost questions.

Interview

Outcomes you will master:

•Answer tokenization interview questions

•Explain BPE clearly

•Connect tokenization to production systems

Duration: 20 min readRead Lesson

Module Status

0%completed

7 Lessonsto complete

Quick Cheatsheet

1.Text → Tokens → IDs → Embeddings is the core entry pipeline structure.
2.1 English word !== 1 token. In fact, subword splits merge fragments.
3.Input + Output = Total. API bills measure the total of both prompts.
4.Token count affects cost & latency. Shorter prompts yield faster generations.
5.Chunking in RAG must measure token indices, not characters lengths.

Syllabus Project

Tokenizer Visualizer Studio

Build a dynamic web app to highlight token borders, trace byte merges, and audit API billing.

Open Lab View GitHub View Project Requirements →

Build What You Learn

Capstone Project: Tokenizer Visualizer Studio

Apply what you learn in these lessons to build a fully functional developer tool. You will build a frontend interface that accepts user string input, and visually highlights token divisions using dynamic HSL styling. The application allows developers to compare how tiktoken (cl100k_base), BERT (WordPiece), and SentencePiece split coding parameters, Unicode characters, non-English text, and emojis.

Example Strings To Bench-Test:

English: "Hello World"
Bengali: "আমি বাংলা শিখছি"
Code: "const userName = getUser()"
Emojis & Symbols: "🔥🚀❤️🤖"
Prompt Format: JSON schemas inputs

Stack Planned

TypeScriptReactTailwind CSStiktoken

Open Lab View GitHub View Project Requirements

Interview Readiness

Interview Defense Checklists

Open Interview Guide

Question 01What is tokenization in LLMs?

Question 02Why do LLMs use subword tokenization?

Question 03What is the difference between token and token ID?

Question 04How does BPE work?

Question 05Why does token count affect API cost?

Question 06Why can non-English text consume more tokens?

Question 07How does tokenization affect RAG chunking?

Question 08How does tokenization affect agent memory?

Open Interview Guide

Production Best Practices Checklist

1Track input and output tokens for API calls

2Estimate API cost before sending large requests

3Use token-aware chunking limits for RAG documents

4Summarize or trim old chat history recursively

5Avoid unnecessary prompt repetition in system instructions

6Test multilingual inputs for token inflation

7Compress large tool outputs before returning them to LLMs

8Implement retry/fallback systems for context window overflows

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