How many categories can I define?

There is no hard limit, but classification accuracy is best with a small set of clearly distinct categories — typically two to ten. Adding a short description or example after each name sharpens the boundaries and reduces ambiguous calls.

What does the confidence score mean?

It is the model's self-reported certainty from 0 to 1 that the text belongs to the chosen category. Treat it as a relative signal rather than a calibrated probability, since LLM confidence is not statistically guaranteed.

What if the model does not return valid JSON?

The tool first tries to parse the response as JSON, and if that fails it falls back to matching one of your category names inside the raw text. You will see the raw output as reasoning so you can spot malformed responses.

Yes. Your key is used only for the direct request from your browser to OpenAI or Anthropic and is never sent to a Gera server or stored. You can revoke it anytime from your provider dashboard.

LLM Output Classifier (BYO-key)

Turn an LLM into a custom zero-shot classifier

You often need to sort free text — support tickets, model outputs, user feedback — into your own categories without training a model. This tool wraps a single API call in a structured classification prompt: you define the labels, paste the text, and get back one category, a confidence score, and a short justification you can review.

How it works

You list your categories one per line, optionally adding a dash and a short description or example to disambiguate them. The tool builds a classification prompt that pins the model to choosing exactly one of your labels and asks it to respond as JSON with a category, a 0–1 confidence, and one-sentence reasoning. It then parses that JSON, normalizes the confidence (accepting either 0–1 or 0–100 formats), and falls back to a name-match if the model wraps the output in prose or code fences.

Tips and notes

Keep categories mutually exclusive and add a one-line example to each — that single change is the biggest accuracy lever for zero-shot classification. For high-volume or production use, switch to a cheaper model like gpt-4o-mini or claude-3-5-haiku and validate a sample by hand to check the boundaries. Low confidence usually signals overlapping or under-specified categories rather than a hard example. Your key never leaves your browser except to call the provider directly, and it is never stored.