Handling Flaky Tests in LLM-powered Applications

Large Language Models (LLMs) pose unique testing challenges due to their inherent non-determinism, susceptibility to prompt injection, and potential for fabricating information, making traditional testing methods inadequate. To address these challenges, new testing strategies such as property-based testing, example-based testing, auto-evaluation, and adversarial testing have been proposed. Property-based testing focuses on verifying specific output characteristics, while example-based testing requires structured output formats. Auto-evaluation uses the model itself to assess the quality of its responses, and adversarial testing attempts to identify vulnerabilities through harmful prompts. Implementing these tests can reduce flaky tests, enhancing the reliability and security of LLM-powered applications. Additionally, practices like setting deterministic outputs, mastering prompt syntax, comprehensive logging, and testing evaluator models are recommended to improve testing efficacy.