dinastafi
Member
Fine‑tuning starts with collecting domain‑specific data whether support transcripts, travel itineraries, voice dialogues, or product descriptions. Ensure data quality: remove noise, label intents/entities, and anonymize PII. Use prompts that reflect real user interactions. Choose which model layers to train: light‑tuning is faster and safer, while full‑fine‑tuning gives deeper adaptation. Split your data into training, validation, and test sets. Begin with a small sample to verify format and flow, then scale up. Monitor perplexity and human evaluation while tuning prompts and hyperparameters. After training, evaluate on unseen user queries heck for accuracy, relevance, bias, and fluency. If the generative AI platform supports prompt‑tuning (like prefix tuning), experiment with few‑shot examples instead of model retraining. For voice or multimodal, fine‑tune pipelines end‑to‑end. Document each version and maintain rollback policies. Regularly retrain with new data or failure cases. And ensure compliance: maintain data lineage and version control for auditing.