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The Definitive LLM Observability Checklist for Healthcare, Life Sciences and Consumer Health

Healthcare, life sciences and consumer health are undergoing a quiet revolution in generative AI. Early applications are showing promise on everything from speeding up preclinical drug discovery and development by better predicting molecular behavior to augmenting how physicians and providers provide care to their patients.

Given the potential harms and regulatory risks intrinsic to applying AI in healthcare, having robust LLM evaluation and LLM observability is critical. 

How can teams deploy generative AI reliably and responsibly – and what should they look for when assessing partners?

Dive into details on essentials like: 

  • Healthcare Use Cases for LLMs 
  • LLM System Evaluations 
  • LLM Traces and Spans 
  • Prompt Engineering 
  • Retrieval Augmented Generation 
  • Fine-Tuning 
  • Embeddings Analysis

Read the Checklist

About the author

Aparna Dhinakaran
Co-founder & Chief Product Officer

Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

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