Large language models introduce unique evaluation challenges that go far beyond traditional accuracy metrics. This workshop focuses on building structured, repeatable approaches to testing LLM behavior so teams can confidently compare options and understand real performance tradeoffs.
To win, teams must evaluate LLMs using task-driven, human-aware methods that surface quality, risk, and reliability differences.
Teams evaluating LLMs frequently run into:
- Undefined evaluation protocols: Test LLMs informally without consistent rules, tasks, or scoring approaches.
- Shallow output assessment: Rely on automated metrics that miss hallucinations, bias, or qualitative failures.
- Unclear comparisons: Compare LLMs using inconsistent prompts or benchmarks, producing unreliable results.
Weak LLM evaluation leads to misleading conclusions and risky model choices.
In this hands-on workshop, your team designs and runs structured LLM evaluations using prompt-based tasks, human review, and consistent benchmarks.
- Define clear protocols for evaluating LLM behavior and outputs.
- Design prompt-based tasks that reflect real application usage.
- Run human-in-the-loop evaluations to assess output quality.
- Analyze results to identify hallucination, bias, and failure patterns.
- Compare LLM performance consistently across shared benchmarks.
- Defining LLM Evaluation Protocols
- Designing Prompt-Based Evaluation Tasks
- Running Human-in-the-Loop Evaluations
- Analyzing Outputs for Hallucination and Bias
- Comparing LLM Results Across Benchmarks
- Establish structured protocols for evaluating LLMs.
- Design realistic prompt-based evaluation tasks.
- Incorporate human review into LLM assessment workflows.
- Identify qualitative risks such as hallucination and bias.
- Confidently compare LLMs using consistent evaluation results.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Prompt evaluation templates, human review workflows, and comparison artifacts