Garbage In, Meaning Out

Every programmer learns the proverb early: garbage in, garbage out. Feed a system bad input and it will hand you bad output, confidently and at speed. It was a warning about data. I have come to read it as a statement about meaning.

Strip away the mystique and a modern AI model is a function. It takes an input and returns an output. It is an extraordinarily sophisticated function, trained on more text than any of us will read in a thousand lifetimes, but it is a function all the same. It does not wake up with intentions. It does not care what you ask. It maps what you gave it to a likely continuation, and stops.

This is not a knock on the technology. It is the technology working as designed. But it has a consequence people skip past in the excitement: the meaning in the exchange is on your side of it. The model supplies fluency. You supply purpose. Without a question there is nothing to answer. Without a standard there is nothing to judge the answer against. The system can produce a thousand competent paragraphs and not one of them is about anything until a person decided it should be.

Intent is the input. Acceptance is the output.

When I work with these tools, I have learned to hold on to two moments. The first is the input — not just the prompt, but the intent behind it: what am I actually trying to make, and why, and for whom. The model cannot supply that. If I am vague, it will be plausibly vague back at me, and the vagueness will look polished, which is worse than looking rough.

The second moment is the one almost everyone drops: acceptance. The output is a proposal, not a verdict. Someone has to read it and decide whether it is true, whether it is good, whether it goes out into the world with a name attached. That someone is me. The instant I let the proposal become the verdict simply because it arrived formatted and assured, I have not saved myself work — I have abandoned the only part of the work that was ever mine.

Engineers know this in their bones, because they have watched confident systems be confidently wrong. The discipline of the field is not trust; it is verification. You do not ship what the machine produced because the machine produced it. You ship it because a person checked it and put their judgment behind it. The check is not bureaucracy. The check is where the value enters the system.

Why this is good news

It would be a bleak world if meaning could be automated, because meaning is the part we actually care about. The relief hiding inside garbage in, meaning out is that it cannot. The machine will get faster and more fluent. It will not start meaning things. The purpose, the taste, the responsibility — these remain stubbornly, permanently, on the human side of the function call.

So the center is the person, and I want to insist that this is an engineering fact before it is a sentiment. The system has no goals of its own; the caller supplies the goal. It has no measure of quality; the caller supplies the measure. Treat the output as finished and you get garbage with good posture. Treat it as raw material for your judgment and you get something that is yours.

Use the tool. Use it hard. Just remember which side of it you are on — the side where things are decided to mean something — and do not, in the rush of all that fluency, quietly resign the post.