Testing applications that use Large Language Models (LLMs) presents unique challenges due to their non-deterministic nature. Unlike traditional software, the same prompt can yield different outputs, making standard testing methods insufficient. This article proposes seven strategies to address this, focusing on an example of extracting structured data like tenant, rent, and notice period from lease texts. Key methods include building a 'golden dataset' with known answers, asserting invariant properties of the output (like valid JSON format or correct data types), and using adversarial testing to probe for weaknesses. AI
IMPACT Provides practical strategies for developers to ensure the reliability and robustness of LLM-powered applications.
RANK_REASON Article discusses methods and tools for testing LLM applications, not a new LLM release or research.
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