Episode
An LLM Evaluation Framework for High-Stakes AI
- Published
- Jun 11, 2026
- Duration seconds
- 993
- Processing state
not_requested
Actions
POST https://stenobird.com/v1/public/podcasts/software-engineering-institute-sei-podcast-series-389154/episodes/an-llm-evaluation-framework-for-high-stakes-ai/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/software-engineering-institute-sei-podcast-series-389154/an-llm-evaluation-framework-for-high-stakes-ai.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Experimentation and validation of LLM performance is critical when building LLM-driven systems that must reliably deliver a service, from customer service chat bots to intelligence analysis tools. To help teams meet the need for rigorous evaluation methods, a research team in the SEI's AI Division led by Violet Turri has developed the Evaluating Large Language Models (ELM) library, which is built on best practices for LLM evaluation and benchmarking. In the latest episode from the Carnegie Mellon University Software Engineering Institute, Turri sits down with Katie Robinson, a design researcher also in the SEI's AI division, to discuss the ELM library, which turns evaluation from an ad-hoc process into a repeatable, extensible framework.