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Stepping Out Of the Comfort Zone Through Domain Adaptationa Deep Dive Into Dynamic Prompting

Part 3/3-�-�-�Deep dive into fine-tuning
Towards Data Science 1:08 pm on June 3, 2024

The document discusses using in-context learning and fine-tuning for domain adaptation with large language models to align generative AI capabilities with enterprise requirements, enabling customization and harmless integration. It highlights the need for choosing appropriate base models based on compliance, ethics, governance guidelines, task-specific behaviors, performance uplift targets, data availability, and preference alignment through reward mechanisms like PPO.

Domain Adaptation:
In-Context Learning & Fine-Tuning:
Base Model Selection:
Performance Targets:
Preference Alignment with RLHF and PPO:

The text falls under two
https://towardsdatascience.com/stepping-out-of-the-comfort-zone-through-domain-adaptation-a-deep-dive-into-dynamic-prompting-4860c6d16224

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Stepping Out Of the Comfort Zone Through Domain Adaptationa Deep Dive Into Dynamic Prompting

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