Tips for Building Better Classifiers

Building a classification model with Labelf is easy — building a great one takes thoughtful design and iteration. This article shares proven strategies for getting the most out of your Labelf models, from writing effective task descriptions to troubleshooting common problems.

Before creating a model, define what you want to learn from your data. A vague goal like "understand our customer interactions" will lead to vague labels. A specific goal like "identify the primary reason customers contact support" or "detect whether a churn mitigation attempt was made" will guide everything from label design to task descriptions.

Ask yourself: what decision will this classification help you make? The answer should shape your entire model design.

The task description is one of the most important inputs for your model. It tells the AI what classification task to perform. Write it as if you're explaining the task to a new team member who has never seen your data:

A well-written task description can be the difference between a good model and a great one, even without any fine-tuning.

Each conversation should fit clearly into one label. If you frequently debate between two labels for the same conversation, that's a sign the labels are too similar or not well-defined enough. Consider merging them or rewriting their descriptions.

Starting with a large number of labels (10+) makes classification harder. Begin with a smaller set of high-level categories (2–5) and expand later if needed. You can always create additional models for subcategories.

If your labels don't cover every possible conversation type, consider adding an "Other" or "Not Applicable" category. This prevents the model from being forced to assign an ill-fitting label. Just be aware that if "Other" becomes too large, it may indicate your label structure needs rethinking.

Label descriptions are crucial — they directly guide how the model classifies conversations. Treat each description as a mini instruction manual:
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Include what qualifies: "The customer's issue was fully resolved — the agent confirmed the fix, and the customer acknowledged satisfaction with the outcome."
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Include what doesn't qualify: "This label does NOT apply if the agent only promised a follow-up without actually resolving the issue in this conversation."
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Give concrete examples where possible: "Examples include: refund processed and confirmed, technical issue fixed and verified, account change completed as requested."
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Address edge cases: "If the conversation ended abruptly before resolution could be confirmed, classify as 'Partially Resolved' rather than 'Resolved'."
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The more detail you provide, the better the model performs — especially when deploying from instructions without fine-tuning.

The Generate labels button in the model creation wizard can suggest labels based on your task description. This is a great way to quickly scaffold your label structure. However, always review and customize the suggestions:

Don't spend too long designing the perfect model before deploying. The fastest path to a good model is:

Real predictions on real data will teach you more about your model's behavior than any amount of upfront planning.

When you're ready to improve your model, don't start from a blank page. Go to the Task Settings tab on your model's detail page and click Analyze & Optimize. This AI-powered feature analyzes your model's current performance — including its confusion patterns — and suggests targeted improvements to your task description and label definitions.

Each suggestion comes with a clear explanation of why the change is recommended, making it easy to understand what's driving the improvement. You can apply suggestions individually, edit them first, or apply all at once. This is often faster and more effective than trying to diagnose issues manually.

When reviewing model predictions, pay special attention to conversations where the model picks the wrong label. The confusion matrix on the Metrics tab shows you exactly which label pairs cause the most errors — and you can click on any cell to view the actual misclassified conversations.

Focus your prompt tuning on these confusion pairs. If the model frequently confuses "Partially Resolved" and "Resolved," rewrite both label descriptions to make the boundary between them explicit. This targeted approach is far more efficient than trying to improve everything at once.

When prompt tuning, you need a small validation set to test against. Make this set representative:

Sometimes a single model can't capture all the nuance you need. Labelf lets you chain models together using model filters during model creation. For example:

This hierarchical approach often produces better results than one model trying to do everything.

Overlapping labels — If "Technical Issue" and "App Problem" frequently overlap, the model will struggle. Merge them or define clear boundaries in the descriptions.
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Vague task descriptions — "Classify this conversation" gives the model nothing to work with. Be specific about what dimension you're classifying on.
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Vague label descriptions — "Positive outcome" is too ambiguous. What counts as positive? For whom? Under what circumstances?
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Too many models for one task — It can be tempting to build many narrow models. Sometimes a single well-designed model with careful labels is more effective and easier to maintain.
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Ignoring model predictions after deployment — A deployed model isn't "done." Review predictions regularly and refine as your data evolves. Use Analyze & Optimize periodically to check for new improvement opportunities.
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Training on stale data — If you fine-tune, make sure your training data reflects current customer behavior, not just historical patterns.

If you need advice on model design, prompt tuning strategy, or troubleshooting, reach out to us at support@labelf.ai or use the chat widget in the bottom-right corner of the screen.