SUMM

Building AI Products That Actually Work - Ben Hylak, Sid Bendre

Introduction and Context 00:04

  • Ben Hylak introduces the topic of building effective AI products and expresses gratitude for the audience's presence.
  • The discussion will focus on product iteration rather than evaluation methods.

Importance of Iteration in AI Products 00:27

  • Iteration is essential for developing functional AI products, as highlighted by Ben's background in tech and AI.
  • Sid Bendre, co-founder of Alie, joins to share insights on creating successful products.

Current AI Product Landscape 01:48

  • The last year has seen advancements in creating highly specialized AI models for specific tasks.
  • Successful examples include ChatGPT, which excels at web searching, though not all products, including OpenAI's Codex, have performed well.

Challenges and Issues in AI Products 02:48

  • Many AI products face unexpected issues, such as miscommunication or inappropriate responses, indicating a need for better iteration and evaluation.
  • Ben shares humorous examples of AI failures to illustrate the unpredictability of current AI systems.

The Role of Evaluation (Eval) in Development 08:34

  • Ben discusses common misconceptions about evaluations, emphasizing that they often do not accurately reflect product quality.
  • Evaluations are limited to known issues and may not capture new, real-world problems.

Defining and Understanding Signals 11:06

  • Signals, both explicit and implicit, are crucial for understanding app performance in AI products.
  • Ben explains the need for ongoing monitoring and refinement of signals to improve AI applications.

Sid Bendre's Framework for AI Product Success 14:13

  • Sid introduces Trellis, a framework designed for systematically improving AI user experiences while maintaining the magic of AI.
  • The framework focuses on discretization, prioritization, and recursive refinement of AI outputs.

Steps to Implementing the Trellis Framework 15:56

  • The process includes initializing output spaces, classifying user intents, and creating semi-deterministic workflows.
  • Prioritization of workflows is based on user impact and sentiment analysis.

Conclusion and Takeaways 18:27

  • The goal is to create repeatable and testable magic in AI products through structured workflows.
  • Both Ben and Sid emphasize the importance of continuous improvement and iteration in the development of functional AI products.
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