Explore the intricacies of AI alignment and domain-specific model fine-tuning in this MLOps Podcast episode featuring Shiva Bhattacharjee, CTO of TrueLaw Inc. Delve into the necessity of fine-tuning versus prompting, and discover the innovative loop of sampling, feedback collection, and fine-tuning that yields superior performance in specialized tasks. Learn about DSPy implementation, RAG strategies, cost-effective embedding fine-tuning, and AI infrastructure decision-making. Gain insights into prompt data flow evolution, the buy vs. build dilemma, and essential tech stack considerations. With 20 years of experience in distributed and data-intensive systems, Bhattacharjee shares valuable perspectives on creating models that excel in domain-specific applications, offering a 7-10x improvement over base models.
Alignment is Real - Fine-Tuning vs. Prompting in Domain-Specific Tasks
Overview
Syllabus
[] Shiva's preferred coffee
[] Takeaways
[] DSPy Implementation
[] Evaluating DSPy risks
[] Community-driven DSPy tool
[] RAG implementation strategies
[] Cost-effective embedding fine-tuning
[] AI infrastructure decision-making
[] Prompt data flow evolution
[] Buy vs build decision
[] Tech stack insights
[] Wrap up
Taught by
MLOps.community
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