Aligning large language models with specific objectives and use cases requires a robust fine-tuning pipeline that ensures both precision and efficiency. Panelists will discuss the technical intricacies of building such pipelines, sharing insights into data curation, model adjustment strategies, and monitoring alignment, while addressing challenges like maintaining model generalization and mitigating risks associated with bias and drift.