Explore strategies for reducing inference costs and improving accuracy when using Large Language Models (LLMs) in this MLOps Coffee Sessions podcast episode featuring Lingjiao Chen. Dive into the concept of FrugalGPT, a flexible LLM cascade approach that optimizes LLM combinations for different queries. Learn about prompt adaptation, LLM approximation, and LLM cascade techniques to achieve up to 98% cost reduction while matching or surpassing the performance of top-tier models like GPT-4. Gain insights into practical implementation strategies, including prompt optimization, query concatenation, and the use of completion caches and vector stores for efficient LLM applications.
FrugalGPT: Better Quality and Lower Cost for LLM Applications
Overview
Syllabus
[] Lingjiao's preferred coffee
[] Takeaways
[] Sponsor Ad: Nayur Khan of QuantumBlack
[] Lingjiao's research at Stanford
[] Day-to-day research overview
[] Inventing data management inspired abstractions research
[] Agnostic Approach to Data Management
[] Frugal GPT
[] Just another data provider
[] Frugal GPT breakdown
[] First step of optimizing the prompts
[] Prompt overlap
[] Query Concatenation
[] Money saving
[] Economizing the prompts
[] Questions to accommodate
[] LLM Cascade
[] Frugal GPT saves cost and Improves performance
[] End-user implementation
[] Completion Cache
[] Using a vector store
[] Wrap up
Taught by
MLOps.community
