Project 1: Token Window Visualizer

Build a tool that visualizes context window usage and shows how messages fill the token budget.

Quick Reference

Attribute Value
Difficulty Level 1: Beginner
Time Estimate 4-6 hours
Language Python or JavaScript
Prerequisites Basic text processing
Key Topics token counts, context limits, truncation

1. Learning Objectives

By completing this project, you will:

  1. Compute token counts for chat histories.
  2. Visualize which messages fit in the window.
  3. Simulate truncation strategies.
  4. Compare costs of different prompts.
  5. Export reports for analysis.

2. Theoretical Foundation

2.1 Context Windows

LLMs can only see a fixed number of tokens. Memory management is about deciding what fits.

3. Project Specification

3.1 What You Will Build

A tool that takes a conversation transcript and shows which messages are retained when the context window fills up.

3.2 Functional Requirements

  1. Tokenizer support for at least one model.
  2. Window simulation with max tokens.
  3. Visualization of included vs excluded messages.
  4. Truncation strategies (oldest-first, summary).
  5. Export of window usage metrics.

3.3 Non-Functional Requirements

  • Deterministic results for identical input.
  • Readable UI or CLI output.
  • Handle long transcripts gracefully.

4. Solution Architecture

4.1 Components

Component Responsibility
Token Counter Count tokens per message
Window Simulator Apply truncation rules
Visualizer Render token usage
Exporter Save report

5. Implementation Guide

5.1 Project Structure

LEARN_LLM_MEMORY/P01-token-window/
├── src/
│   ├── tokenize.py
│   ├── simulate.py
│   ├── render.py
│   └── cli.py

5.2 Implementation Phases

Phase 1: Token counting (2-3h)

  • Count tokens per message.
  • Checkpoint: totals match tokenizer output.

Phase 2: Window simulation (1-2h)

  • Apply truncation strategies.
  • Checkpoint: oldest messages drop first.

Phase 3: Visualization (1-2h)

  • Render window usage.
  • Checkpoint: report shows included/excluded messages.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit token counts stable totals
Integration window truncation correctness
Regression export schema stable

6.2 Critical Test Cases

  1. Window limit drops earliest messages.
  2. Summary strategy keeps key facts.
  3. Exported report validates.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Token mismatch counts off align tokenizer version
Hidden whitespace incorrect counts show whitespace tokens
Too much truncation missing context tune window size

8. Extensions & Challenges

Beginner

  • Add multiple model tokenizers.
  • Add prompt presets.

Intermediate

  • Add summary-based truncation.
  • Add cost estimation.

Advanced

  • Add dynamic window allocation by role.
  • Add interactive UI.

9. Real-World Connections

  • Chatbots need window awareness to avoid context loss.
  • Cost control depends on accurate token budgets.

10. Resources

  • Tokenizer documentation
  • LLM context window guides

11. Self-Assessment Checklist

  • I can compute token counts reliably.
  • I can simulate truncation strategies.
  • I can visualize context usage.

12. Submission / Completion Criteria

Minimum Completion:

  • Token counter and window simulator

Full Completion:

  • Visualization and export

Excellence:

  • Summary-based strategy
  • Multi-model comparison

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LLM_MEMORY.md.