AI Classification Workflow
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.
The problem
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.
Recommended workflow
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.
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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.
Goal The clean unit to classify, separated from the noise.
Open this step in Extraction Prompt Generator -
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.
Goal One label from your defined set, chosen consistently.
Open this step in Data Classification Prompt -
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.
Goal A validated label safe to automate on.
Open this step in AI Output ValidatorResource Validate Classification OutputTool AI Output Validator
Expected outcome
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.
Best for
- Routing or triaging text at scale
- Ticket, intent, or moderation classification
- Classification that feeds an automated pipeline
Not for
- Analyzing feedback into themes for a human — use the AI Customer Feedback Analysis Workflow
- A one-off categorization you'll read yourself
FAQ
How is this different from the AI Customer Feedback Analysis Workflow?
Feedback analysis classifies as a step toward human-readable themes and priorities. This builds a reliable, validated classification step for automation — the output is a clean label a router or pipeline consumes, not a summary a person reads.
Why validate the label?
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.
Do I always need the extraction step?
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.