// Interactive lab

AI Token Budget Lab

Deterministic AI token budget simulator for exploring approximate token use, context pressure, retrieval load, retries, agent loops, latency, and classroom or team-scale cost.

A local simulator for teaching token usage, context pressure, retrieval overhead, retries, agent loops, latency, and classroom-scale cost.

Try lab Back to labs

This browser lab estimates how prompt anatomy, retrieval, tool output, retries, model assumptions, and repeated classroom or team use can change an AI workflow budget.

Boundary

The lab is deterministic and local. It does not call an AI API, use an API key, contact a tokenizer service, or send user-entered text out of the browser.

How to read it

Token counts, context pressure, latency, and cost are approximations. Real tokenizers, billing rules, infrastructure, model behavior, and application designs vary. The editable profiles are teaching assumptions, not provider claims.

What it teaches

Prompt sections compete for the same context window.
Retrieval can help when the signal is focused, but noisy chunks can bury the useful material.
Retries and agent loops can multiply both cost and latency.
Small per-run estimates can become meaningful when many students, teams, or sessions repeat the workflow.

Use Probability Signal Simulator for probability updating, Chaos Divergence Explorer for feedback and forecast limits, and Practical AI Implementation for the adoption context behind token budgets.