Bakery AI Helper: how it finds a problem, and what it costs
Asha’s bakery sells bread online. Today the orders page is slow. Watch an AI helper look into it, then see what that costs. Sample data: every name and number here is made up for teaching
Ask the helper
Pick a question. A good helper shows what it looked at, so a person can check it.
How the helper works, step by step
Think of a good librarian. You ask a question, they find the right books, they tell you the answer and say where they found it, and the library head signs off before anything changes.
What it costs, and why the cheapest is not always cheapest
An AI is billed by the amount of text it reads and writes. Small models cost less but get more answers wrong. Wrong answers cost people time. Move the sliders and see.
Sample price list per million words-ish (tokens): small $0.25 in / $1.25 out, medium $3 / $15, large $15 / $75. Each question reads 3,000 and writes 600. Real prices differ and change: check each provider’s own price page.
Words explained, and where this stops being true
- Model
- The AI brain that reads a question and writes an answer. Small, medium and large models are like a junior, a regular and an expert: faster and cheaper, or slower and more careful.
- Token
- A small piece of text, about three quarters of a word. AI bills are counted in tokens.
- Trace ID
- A tag on each answer, like a receipt number, so you can find what the helper used.
- Human in the loop
- A person approves anything that changes something real, like a school needs a permission slip for a trip.
- Escalate
- Try the small model first. If it fails, ask a bigger one. Like asking a teacher’s helper before the head teacher.
- Rework
- Time a person spends fixing a wrong answer. It is part of the true cost.
The limits of this demo
- All numbers are made up to teach the idea. They are not measurements of any real system.
- The “hard questions stay hard” slider is a simple guess at something messy. Real systems need real tests.
- A cheaper path is only better if its answers are checked. Zero failures in a few tests is not proof (see the article linked below).
- Prices change often. Always read the provider’s own page.
For engineers: the maths used here
cost per try = (tokens_in × price_in + tokens_out × price_out) / 1,000,000
With escalation (small, then medium, then large), let f be the chance a try fails. A failed question fails the next try with probability r + (1 − r) × f_next, where r is how often hard questions stay hard. Expected AI cost is the cost of the small try plus the chance of reaching each next tier times its cost. Total cost per question = AI cost + P(final failure) × cost of fixing it. Cost per successful task = AI cost / P(success).
Levers not modelled here: caching repeated work, shorter prompts, routing by task, and tests that decide when to escalate. Read more: AI caching, explained simply, 50 tests passed. Is it safe?, Autonomy is earned.
Text version of the demo
Everything above in plain text, for readers who prefer it or use a screen reader.
The three questions and answers (sample data)
- Why is the orders page slow today? Two things stand out. The orders page loads 14 large photos, so it sends far more data than usual. The shop’s database is also asked the same question many times in a row. Photos are the bigger cause. It looked at: page speed log, list of today’s changes, database query counts (all sample).
- What changed since yesterday? Yesterday at 4 pm, a new photo gallery was added to the orders page. Nothing else on the list touches that page. The timing matches when pages started to feel slow.
- What should we do first? Shrink the photos and load them only when someone scrolls to them. This is the smallest change. A person must approve it before it goes live.
The five steps
- A person asks a question in plain words.
- The helper reads only the information it is allowed to read.
- The model works out the likely cause from those facts.
- The answer lists what it used and carries a receipt number.
- Anything that changes something real waits for a human yes.
The cost lesson
A small model costs less per question but gets more answers wrong, and wrong answers cost people time. Whether trying the small model first and escalating to a bigger one saves money depends on how often a hard question stays hard. With the made-up numbers on this page, at 50% the large model alone is cheapest overall, and at 20% small-first escalation is cheapest. Real systems need real tests.