How to Synthesize Domain-Specific Knowledge for Generative AI-model fine-tuning

This session explores techniques for synthetic domain-specific data generation to bridge the AI data gap, enabling enterprises to fine-tune AI models even when real-world data is scarce or proprietary. Attendees will gain insights into novel methods for generating, validating, and transferring domain knowledge into AI models for improved performance in industry-specific scenarios.

#### Key Focus Areas:

✅ Synthetic Domain Knowledge Generation (SDKG)

• Leveraging large language models (LLMs), knowledge graphs, and AI-driven simulation environments to create realistic domain-specific datasets.
• Approaches for generating structured and unstructured synthetic data while maintaining contextual accuracy.

#### Who Should Attend?

• AI researchers & ML practitioners
• Data scientists working in regulated industries
• Enterprise leaders seeking domain-adaptive AI solutions
• AI product managers building custom AI models for industry applications