Shipping AI That Works: An Evaluation Framework for PMs – Aman Khan, Arize

This talk focuses on mastering AI evaluation, moving from initial development to production. It emphasizes that even the best large language models (LLMs) require a robust testing framework due to issues like hallucinations and performance degradation with changes. Effective evaluation helps answer critical questions about model selection, cost-effectiveness, brand consistency, and ongoing improvement, ultimately reducing development time, costs, and enabling faster iteration.

This talk addresses the gap between AI-generated code prototypes and production-ready software. It introduces Sculptor, an experimental coding agent environment designed to build trust in AI-generated code by focusing on identifying and preventing defects. The core thesis is that AI should be leveraged not just for code generation but also for ensuring its quality and reliability.

This talk introduces the concept of evals at the application layer, distinguishing them from traditional unit tests. It emphasizes that Large Language Models (LLMs) can be unreliable, leading to unexpected failures in AI applications even when basic functionality appears to work. The core thesis is that robust evals are crucial for building reliable AI products by systematically testing and measuring performance across a spectrum of user-driven scenarios.

This talk explores the complexities of transitioning a SaaS company to an AI-first model, emphasizing that it's an evolutionary journey rather than a binary switch. It proposes a framework focusing on strategy, ways of working, and people to navigate this transformation. The core thesis is that becoming AI-first requires reimagining product development, embracing ambiguity, and fostering a cultural shift across the entire organization, not just within AI teams.

This talk discusses the challenges and solutions for implementing and scaling Message Communication Protocol (MCP) clients within a large organization. It highlights how the rapid evolution of AI models capable of tool calling led to integration chaos, with duplicated functionality and inconsistent interfaces across services. The solution proposed is to standardize on MCP for providing model context, treating it as a plumbing layer for integrations rather than a competitive differentiator.