The Definitive LLM Observability Checklist for Retail & Ecommerce

With holiday shopping eclipsing $222.1 billion in the U.S., the potential impact of applying AI in digital commerce is palpable. As early-adopters of generative AI see outsized gains, many are finding that having robust LLM evaluation and LLM observability in place is critical. 

Informed by experience working with top retailers and ecommerce destinations with successful LLM apps deployed in the real world, this checklist covers essential elements to consider when assessing an LLM observability provider.

Dive into essentials, including:

  • Common LLM Use Cases for Retail
  • LLM System Evaluations 
  • LLM Traces and Spans 
  • Prompt Engineering 
  • Retrieval Augmented Generation 
  • Fine-Tuning 
  • Embeddings Analysis 
  • Platform Support

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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