LLMs are not every ML problem
Chat interfaces are powerful, but they are not the default for every prediction problem. Ranking, fraud scoring, forecasting, churn propensity, and classical classification often need purpose-built features and models — not a conversational wrapper.
Ask: do we need language generation, or do we need a score, rank, or label we can act on in product logic? The second case is usually classical ML or a small specialized model.
Data reality check
If you cannot define labels or access historical outcomes, you do not have an ML project yet. You have a data project.
Before modeling:
- Define the decision the prediction will change
- Name the label and who owns labeling quality
- Check leakage (using future data to predict the past)
- Confirm you can serve features at prediction time
Skipping this step produces models that look good in notebooks and fail in production.
Ship a baseline first
Heuristic rules or a simple logistic baseline set the bar. Complex models must beat them on offline metrics and on a product KPI (conversion, loss prevented, time saved).
Document the baseline so “model improved accuracy by 2%” can be compared to “model reduced false declines by X.”
Monitoring after launch
Track data drift, performance by segment, and the cost of mistakes (false positives vs false negatives). Silent model decay is a product incident, not a research footnote.
Plan a retrain or recalibration cadence before launch day — not after the first spike in complaints.
Where XYRONEXT fits
Our AI development practice covers LLM features when language is the product surface, and classical ML when the product needs reliable scores and rankings. We start from the decision and the data, then choose the model class.