Originally published on AI Tech Connect.
What you need to know Few-shot prompting — pasting a handful of worked examples into a prompt before asking the model to handle a new case — is one of the oldest tricks in the prompt-engineering toolbox, and it remains one of the most reliably useful. It is also one of the most sloppily implemented. Most production prompts ship with either too many examples dragged in from an early experiment and never revisited, or too few, chosen at random from whatever data was closest to hand, in whatever order they happened to be typed. Both mistakes cost real accuracy and real money, and both are fixable with a few hours of deliberate work. This guide is a practical walk-through of what the research and the production evidence actually say: how many examples typically earn their keep before returns…
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