Data preprocessing cleans and formats raw data before it reaches an AI model. This includes removing errors, standardizing text, handling missing values, and transforming data into the structure the model expects.
The quality of preprocessing directly affects model performance. A model trained or run on inconsistent, noisy data will produce inconsistent, noisy outputs. Preprocessing is one of those foundational steps that gets less attention than model selection or architecture but often has more impact on real-world performance.
