Enterprises are moving beyond generic foundation models and focusing on how to customize AI for specific workflows, data, and business contexts. This panel dives deep into two core techniques—fine-tuning and prompt engineering—that enable organizations to extract maximum value from generative models. Experts will share strategies for balancing cost, control, and performance when adapting models to domain-specific use cases, from customer support to design to code generation. Discussion will cover instruction tuning, retrieval augmentation, and structured prompt frameworks for consistent outputs. Attendees will walk away with a clear understanding of how to align model behavior with business goals, when to fine-tune vs. when to prompt-engineer, and how leading teams are operationalizing these methods to build smarter, more differentiated AI systems.
