Section 188 · Appendices, Tools, Templates, and Reference
Appendix: Eval Case Examples for Prompts, Chatbots, and LLM Inputs
A strong LLM eval suite needs normal requests, weird requests, hostile requests, and inputs the system should not answer.
eval case examples
What to do
- Use this checklist when building a chatbot eval suite.
- Use AI to generate more cases, but do not let AI silently define the whole eval distribution.
- Define runnable checks that exercise eval case examples.
Evidence to preserve
- Include obvious secrets, cross-user leakage, sensitive memory, and cases where the user asks for someone else's information.
- Preserve the inputs, versions, configurations, raw outcomes, and results for eval case examples needed to reproduce work on Appendix: Eval Case Examples for Prompts, Chatbots, and LLM Inputs.
- Report results for eval case examples by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Every eval case should have metadata: intent, risk class, expected behavior, allowed variation, hard blockers, source, slice, severity, and whether it came from synthetic generation, human design, red-team work, or production trace mining.
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Cite this page
Jason Arbon. "Appendix: Eval Case Examples for Prompts, Chatbots, and LLM Inputs." Testing AI Knowledge Edition, section 188.
https://jarbon.ai/testing-ai/knowledge/ch188-eval-case-examples.html