How to Write Prompts for Text Classification

What is Text Classification Prompting?

Picture this: you're a support operations lead with 3,000 unlabeled support tickets and a deadline to route them into the right queues by end of week. Doing this manually would take days of tedious reading. Writing a good prompt for text classification can do it in an afternoon, but only if you set up the categories correctly first — which is the part most people skip in a rush to just get the tickets moving out of the backlog.

Prompts for text classification ask the model to assign one or more predefined labels to a piece of text — sentiment, topic, urgency, intent, whatever categories matter for your workflow. The technique itself is simple. The part that actually determines quality is how precisely you define the categories before you ever write the classification prompt itself, and that's exactly the step most teams rush through.

⚠️ Common mistake: Writing a classification prompt before your categories are clearly defined and mutually exclusive. If two categories could reasonably apply to the same piece of text, the model will be inconsistent about which one it picks, and that inconsistency isn't a prompting failure — it's a category design failure that no amount of prompt tweaking will fully resolve.

Why It Matters

Bad classification compounds downstream. A support ticket misrouted to the wrong team doesn't just create one delay — it creates a re-routing step, a longer resolution time, and a customer who's now waited twice as long as they should have for a resolution. At volume, small classification error rates translate into real operational cost that shows up in customer satisfaction scores months later.

The upside is proportional. A support operations lead at a software company who rebuilt her classification prompt around clearly defined, non-overlapping categories cut ticket misrouting by more than half, which directly reduced her team's average first-response time because tickets stopped bouncing between queues before landing somewhere useful for the customer waiting on the other end.

Defining Categories That Actually Work

Before writing any classification prompt, write out your categories with one clarifying example each — not just a label, but a sentence showing what belongs in it.

Categories:
- Billing: anything about charges, invoices, refunds, or payment methods
- Technical: anything about the product not working as expected, bugs, or errors
- Account: anything about login, password, or account settings
- Other: anything that doesn't clearly fit the above

Classify the following ticket into exactly one category. If it could fit two categories, choose the one representing the customer's primary concern, not every topic mentioned.

What this does: the "primary concern, not every topic mentioned" instruction handles the common case where a ticket mentions billing and a technical issue together, giving the model a clear rule for resolving that overlap instead of leaving it to guess inconsistently between runs, which is exactly the kind of inconsistency that erodes trust in an automated classification system over time.

⚡ Pro tip: Always include an "Other" or "Unclear" category, even if you expect to use it rarely. Forcing every input into a defined category when none really fits produces confidently wrong classifications that are harder to catch than an honest "doesn't fit" response, which at least tells you where your category scheme has a gap.

⚡ Pro tip: Test your category definitions against 10-15 real examples before trusting the classification at scale. Categories that seemed clearly distinct on paper often turn out to overlap once you see actual messy real-world text hitting the boundaries between them in ways you didn't anticipate while writing the definitions.

Handling Multi-Label and Confidence Cases

Some classification tasks genuinely need more than one label per item — a support ticket that's both a billing issue and a technical bug, for instance, where routing it to only one team would leave the other issue unaddressed. Specify this explicitly rather than forcing single-label output onto a genuinely multi-label problem:

This ticket may belong to more than one category. List all that apply, ranked by relevance, with the most relevant category first.

What this does: prevents the model from arbitrarily picking just one category when the reality is genuinely multi-label, which would otherwise lose real information about tickets that need attention from more than one team at once.

For any classification feeding into an automated workflow, request a confidence score alongside the label, since this is what lets you route uncertain cases differently from confident ones instead of treating every classification as equally reliable:

Also output a confidence score (high/medium/low) for the classification. If confidence is low, briefly note why.

A data operations manager at an insurance company uses this to route low-confidence classifications to a human reviewer automatically, while letting high-confidence ones flow through the automated pipeline untouched. This hybrid approach catches most of the ambiguous cases without requiring a human to review every single classification, which would defeat the purpose of automating the process in the first place.

⚡ Pro tip: Periodically sample your high-confidence classifications too, not just the low-confidence ones. A model that's consistently overconfident about a specific type of error won't flag it as low-confidence, so occasional spot-checks of the "confident" bucket catch a different category of mistake than confidence flagging alone ever will.

⚡ Pro tip: If your categories or their definitions change over time — which happens naturally as a product or support process evolves — update your classification prompt's examples immediately. A prompt still referencing outdated category definitions will keep producing classifications based on rules that no longer match your actual workflow, and this kind of drift is easy to miss until someone notices tickets landing somewhere that no longer makes sense.

Common Mistakes

The most common mistake, by far, is under-specifying category boundaries and then blaming the model when classification is inconsistent. If a human reviewing the same items would also struggle to consistently pick between two categories, no amount of prompt refinement fixes that — the categories themselves need to change, usually by either merging the overlapping categories or adding an explicit tiebreaker rule.

⚠️ Common mistake: Assuming a classification prompt that worked well during testing will hold up indefinitely without monitoring. Classification accuracy tends to drift as the actual content you're classifying shifts over time — new products, new support issues, new customer language — even when the prompt itself hasn't changed at all. A quarterly review of a sample of classifications, checked against what a human would actually assign, catches this drift before it becomes a real operational problem.

There's a related mistake worth naming: treating classification as a one-and-done setup rather than a system that needs occasional recalibration as your business changes over time. A support operations team that launches a new product line often needs a new category or a redefinition of an existing one, and prompts left unchanged after that kind of shift start silently misclassifying an entire new category of ticket into whatever existing bucket seems closest.

Another frequent issue is skipping the step of testing against genuinely ambiguous real examples, and instead validating only against clean, obviously-categorizable text pulled from an easy sample set. Clean examples rarely reveal the actual weak points in a category scheme — it's the messy, borderline cases that expose where two categories overlap or where your "Other" bucket is doing more work than it should be.

Conclusion

Good text classification prompting is mostly a category design problem wearing a prompting costume. The prompt itself matters, but clear, mutually exclusive, example-anchored categories do more to determine accuracy than any amount of clever instruction wording ever will. It's tempting to treat classification failures as a prompting problem to solve with better phrasing, when the actual fix usually lives one level up, in how the categories themselves were defined.

The support operations lead mentioned earlier put it well: her biggest accuracy gains didn't come from tweaking her prompt's wording at all. They came from sitting down with her team for an hour and actually agreeing, in writing, on what belonged in each category — something that turned out to be surprisingly contentious even among people who'd been manually routing tickets the same way for years without ever writing the logic down.

That's worth sitting with for a second: if your own team can't quickly agree on where a borderline example belongs, that's not a small detail to sort out later on. It's the actual source of whatever inconsistency you're seeing in your classification results, and it's worth fixing before you spend more time adjusting prompt wording that was never the real problem in the first place.

Once you've built categories and a classification prompt that hold up against real, messy examples, that combination is worth protecting as a shared team asset. PromptABCD is useful here for versioning both the category definitions and the classification prompt together, so if your categories evolve, the prompt and its documentation evolve with them instead of drifting out of sync.