Do I need to understand different AI models to use AI well?

Not by name. You need one principle: match the tool to the job. Cheap and fast for high-volume, low-stakes work; slower and stronger where the reasoning matters or the output is going to a customer.

What is model stacking?

Running a cheap, fast model for the bulk of the work and bringing in a stronger one only to review, unblock or approve before anything ships. You get most of the quality at a fraction of the running cost, which matters once a job runs hundreds of times a month.

How does Fordi decide which model to use?

By the job. The main audit analysis — the part making judgements about your copy and operations — uses a stronger model. A narrow, tightly-bounded pass that writes at most two performance issues uses a smaller, faster one. Same principle we would apply in your business.

Match the AI model to the job

Most small businesses I meet have picked one AI tool and now use it for absolutely everything. Which is a bit like owning one spanner and being quietly annoyed that not every bolt fits it.

There is no single best AI, in the same way there is no single best vehicle. There is a fast one, a cheap one, a strong one and a careful one, and the skill — the entire skill, honestly — is knowing which job you are on.

Sort your work into three piles

You do not need to learn model names or read benchmark charts. You need to sort the work, which you can do on the back of an envelope.

Volume work — high repetition, low stakes, obvious when it is wrong. Tidying descriptions, tagging enquiries, first-pass sorting of an inbox. Cheap and fast wins. Paying premium rates here is money on the floor, because it runs hundreds of times a month.
Judgement work — where being subtly wrong costs you. Anything going to a customer, anything with a number in it, anything where tone matters. This is where a stronger, slower model earns its keep.
Irreversible work — sending, publishing, promising, pricing. No model, at any price, without a person. Not because the technology cannot, but because the cost of the rare bad one is not symmetric.

The pattern worth stealing

The most useful arrangement I have seen — and it is common enough now to be close to standard practice — is stacking. A cheap model does the grinding volume. A stronger one is called in to review before anything ships, or when the cheap one is going in circles. You get most of the quality for a fraction of the running cost.

The mental model is a capable junior doing the work and an experienced head checking it before it goes out. You would not put your best person on data entry. You would also not let the data entry go out unread.

We do this in our own tools

Our audit works this way. The main analysis — reading your homepage and judging your copy and how manual your operation is — runs on a stronger model, because that is judgement and it goes in front of you. A separate, tightly-bounded pass that writes at most two performance issues runs on a smaller, faster one, because the job is narrow and capped in code. Same reasoning as the scoring rules we publish: put the care where being wrong would actually cost something.

One caution before you go optimising. Every lab publishes the benchmark it happens to win, so treat all of it as directional and test on your own work. Your invoices and your customers are the only benchmark that pays you.

Overall: the question is never “which AI is best”. It is “what is this job worth getting wrong”. Answer that and the tool picks itself. If you would like a view on which jobs in your business are which, that is what the free audit is for.

You are probably using the wrong AI for the job