MLOps.community Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320
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Jun 3, 2025 
Raza Habib, CEO and Co-founder of Humanloop and a PhD in Machine Learning, shares insights on enhancing AI product accuracy by shortening evaluation feedback loops. He discusses the evolution of evaluation methodologies in AI, the complexities of large language models, and the importance of collaboration in overcoming AI challenges. Raza highlights how integrating user feedback can refine model performance and improve user satisfaction, particularly in customer support and performance management. His ideas on prompt engineering and the emerging role of AI in personalized recommendations are also enlightening.
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Product Metrics Equal Evals
- Product metrics and evals serve the same purpose: measuring AI system quality.
- They provide proxies for user outcomes and help monitor system performance in development and production.
Evolution of LLM Applications
- As LLM models grow smarter, use cases shift from simple tasks to complex, agentic applications.
- HumanLoop evolved from monitoring production to full tracing, observability, and iterative evaluation.
Iterate Prompts with Team Collaboration
- Use in-production tracing and observability with evaluators to monitor LLM systems.
- Allow both technical and non-technical team members to iterate on prompts and configurations swiftly.