An LLM exam designer shares their strategy for creating robust tests, emphasizing that effective exams should focus on edge cases and potential failure points rather than straightforward scenarios. The designer advocates for building tests around worst-case accidents, such as shipping incorrect items, shipping items twice, or shipping unordered goods. The strategy includes planting ambiguous queries, typos, and data with near-twins to simulate real-world complexity, and crucially, testing how the LLM's learning capabilities might inadvertently cause errors. AI
IMPACT Provides insights into effective testing methodologies for LLM applications, particularly for order-processing systems.
RANK_REASON The item is an opinion piece discussing best practices for testing LLMs, not a release or research paper.
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