Post-Training Mastery for Enterprise AI

Mastering post-training is becoming increasingly essential for AI teams aiming to adapt pre-trained foundation models for specific applications. This involves techniques such as supervised fine-tuning using curated data sets and advanced methods like reinforcement learning from human feedback, which is crucial for aligning AI systems with their intended use cases. An important observation is that traditional academic benchmarks often do not align with real-world performance, leading organizations like Databricks to develop tailored benchmarks for enterprise applications. Companies are encouraged to prioritize creating their own evaluation tools to ensure optimal model selection and application efficacy.