Aligning large language models to specific objectives demands fine-tuning pipelines that are both precise and scalable. In this session, panelists will unpack the technical architecture behind these pipelines - covering data selection, model adjustment techniques, and ongoing alignment monitoring - while tackling challenges like preserving generalization and minimizing bias or drift.