Classification prompt builder
Reliable LLM classification depends on three things: an explicit label set, tie-breaking rules so the model never invents a label or refuses, and a strict output format so the result is machine-parseable. A loose prompt like “is this positive or negative?” works in a demo but breaks in production. This builder produces a structured prompt that holds up across thousands of inputs.
How it works
You define the labels the model must choose from and, optionally, a few
examples in text => label form for few-shot prompting. The tool wraps these
in a prompt that pins the model to your label set, adds tie-breaking and
fallback rules, and specifies either a bare label or a JSON object with a
confidence score. The example block is rendered in the exact output format you
chose, so the model’s pattern-matching reinforces the format you want.
Tips and notes
- Keep labels mutually exclusive. Overlapping labels force arbitrary choices and lower agreement. If two labels often co-occur, consider a multi-label setup instead.
- Use JSON mode for pipelines. The confidence score lets you auto-accept high-confidence predictions and route the rest to a human.
- Three to five examples is usually enough. Cover your trickiest edge cases rather than the obvious ones — the obvious cases rarely need demonstrating.
- Validate the output. Even with strict instructions, check that the returned label is in your set before using it downstream.