Prompt engineering, testing, and production reliability A prompt can work perfectly in the example you used to write it and fail as soon as a real user does something unexpected. They omit information you assumed would be present. They give two instructions that conflict. They paste malformed data. A retrieved document discusses the right topic without containing the answer. A tool is unavailable. The output becomes longer than expected and breaks the schema. Before a prompt becomes part of a production workflow, it helps to test the cases that are most likely to expose what the prompt has been quietly assuming. Start by defining what success actually looks like “Give a good answer” is difficult to test. A production prompt needs a more observable target. Should the model extract five fields? Produce valid JSON? Refuse when required information is missing? Use only supplied sources? Ask a clarification question under specific conditions? Keep the answer below a defined l...

Overthinking, uncertainty, and decision-making Overthinking a decision can feel productive because you are still working on the problem. You compare another option. Read another review. Imagine another scenario. Ask another person. Reopen the spreadsheet. Search for one more piece of information that might finally make the choice feel certain. Sometimes more thinking really does improve a decision. Sometimes the decision has stopped receiving useful new information and the thinking continues anyway. The difficult part is knowing which situation you are in. Ask what information is still missing Before doing more research, try to name the missing information precisely. “I need to know more” is too broad. “I need to know whether the new job includes remote work,” “I need an accurate estimate of the renovation cost,” or “I need to know whether two customers will actually pay this price” gives you something that can be investigated. If you cannot name what information you...