Accuracy scores and leaderboard metrics look impressive—but production-grade AI requires evals that reflect real-world performance, reliability, and user happiness. Traditional benchmarks rarely help you understand how your LLM will perform when embedded in complex workflows or agentic systems. How can you realistically and adequately measure reasoning quality, agent consistency, MCP integration, and user-focused outcomes?
In this practical, example-driven talk, we'll go beyond standard benchmarks and dive into tangible evaluation strategies using various open-source frameworks like GuideLLM and lm-eval-harness. You'll see concrete examples of how to create custom eval suites tailored to your use case, integrate human-in-the-loop feedback effectively, and implement agent reliability checks that reflect production conditions. Walk away with actionable insights and best practices for evaluating and improving your LLMs, ensuring they meet real-world expectations—not just leaderboard positions!
---
Benchmarks and leaderboards are helpful—but they rarely reflect the realities of production AI. Evaluating real-world performance demands deeper insight into reasoning quality, agent reliability, user satisfaction, and integration with agentic systems and MCP (Model Context Protocol).
This hands-on workshop teaches you tangible evaluation methods using popular open-source frameworks (GuideLLM, lm-eval-harness, OpenAI Evals). No prior evaluation expertise required!
You’ll learn how to:
- Build custom evaluation workflows beyond traditional accuracy benchmarks.
- Evaluate reasoning skills, consistency, and reliability in agentic AI applications.
- Integrate human-in-the-loop assessments for better user-aligned outcomes.
- Validate MCP and agent interactions with practical reliability tests.
Whether you're deploying chatbots, copilots, or autonomous AI agents, robust evaluation is critical. Join us to learn actionable strategies to confidently deploy your LLMs in real-world applications.
---related links---
https://www.linkedin.com/in/taylorjordansmith/https://www.redhat.com/en/products/ai