Most professionals who use AI tools regularly hit the same wall: you know roughly what you want the model to do, but getting the wording right feels like guesswork. One phrasing works okay. You tweak it, something else breaks. A colleague suggests something different. Twenty minutes later you have a
Most of us have learned the same prompting trick: give the AI a few examples before asking your actual question. It works. But it creates a persistent problem — someone has to write those examples, curate them, keep them relevant. For a one-off task in a familiar domain, that'
We spend a lot of time crafting prompts. We pick examples we think are representative, write out reasoning steps, and hope the model gets it. Then we run it on actual work — financial analysis, contract review, technical troubleshooting — and find it stumbles on exactly the cases that matter most. The