Build a text classification step you can automate on — pull out the unit to classify, assign a label from a fixed set, and validate the label is one you actually allow.
Classification that drives automation has to be boringly reliable — and models are not boring by default. Ask one to categorize and it'll invent a label that isn't in your set, pick a different one for near-identical inputs, or hedge with two answers when your router needs one. For routing, ticket triage, or moderation, that's a broken pipe. A dependable classification step pulls out exactly what's being classified, assigns a label from a fixed set, and validates the answer is actually in that set before anything downstream acts on it.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Isolate what's being classified
Pull the specific unit to classify out of the surrounding noise, so the label is assigned to the right thing rather than the whole messy blob.
Assign a label from a fixed set
Classify against an explicit, closed set of labels with tie-breaking rules, so the model picks one allowed answer instead of inventing or hedging.
Validate the label is allowed
Confirm the returned label is actually in your set and the output shape is right, before a router or pipeline acts on it.
A classification step that returns one allowed label, consistently, validated before anything downstream acts — reliable enough to route tickets, triage, or moderate on, instead of a guess you have to recheck.
This builds a validated classification step for automation: the output is one clean label a router, triage queue, or moderation pipeline consumes directly. The Feedback Analysis Workflow classifies as a stepping stone toward human-readable themes, sentiment, and priorities a person reads and acts on.
It outputs one label drawn from your fixed, closed set — the single answer a router or pipeline acts on, in the shape you defined. Step 3 validates that label is actually in the allowed set before anything downstream uses it, so a stray or malformed label gets caught rather than shipped.
Work the three steps in your own AI tool: use the extraction prompt to isolate the unit to classify, the classification prompt to assign one label from your fixed set with tie-breaking rules, then the output validator to confirm the label is allowed. NewPrompt supplies the prompts and order; you run and review each.
First tighten Step 3: validation against your allowed set catches out-of-set or malformed labels before they route, but it does not guarantee the AI's choice is correct. For genuinely wrong labels, sharpen the fixed set and tie-breaking rules in Step 2, and confirm Step 1 isolated the right unit. You own the final review.
Because a model will occasionally return a label outside your set or in the wrong shape, and an automated router can't tell. Validating against the allowed set is what keeps a stray label from breaking the pipeline.
When the input is already the thing to classify, you can skip it. It earns its place when the label belongs to a piece buried in a larger, noisier input.
Complete build journeys that include this workflow as a stage.
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