AI Skills & How They Work
Data Preprocessing for AI
Data preprocessing is the unglamorous work that determines model quality: cleaning, normalizing, chunking, and labeling the data before training or retrieval. Most AI failures blamed on models are actually data problems. Good preprocessing is repetitive, specific, and worth more than a better model.
Punti chiave
- Garbage in, garbage out: model quality is bounded by data quality; no architecture fixes bad inputs.
- Cleaning comes first: remove duplicates, fix encoding, normalize formats, and handle missing values deliberately.
- Chunking is a design decision: size, overlap, and boundaries determine what retrieval can find.
- Labels are the truth: inconsistent or noisy labels cap accuracy no matter how good the model is.
- Balance matters: skewed datasets produce skewed models; resample or weight deliberately, not by default.
- Privacy must be handled before training: remove PII, respect licenses, and document provenance.
- Version your data: track datasets, preprocessing code, and model outputs together for reproducibility.
- Measure data, not just models: track coverage, freshness, and label agreement as first-class metrics.
Domande frequenti
How much data do I need?
Enough to cover the variation in production inputs; quality and coverage beat raw volume.
Can preprocessing fix a bad model?
No, but it often fixes what looked like a model problem; always check data first.
Should I anonymize before or after chunking?
Before, and verify no PII leaks through context or metadata.
How do I know my data is good enough?
Run evals on a held-out set; if performance plateaus, improve data before tuning the model.
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