LLM Client Testing: Hand-Written Fakes Outperform Mocking Libraries

This article discusses a strategy for testing Large Language Model (LLM) clients in software development by creating a hand-written fake object instead of relying on mocking libraries. The author argues that for stateful dependencies like LLM clients, which are called repeatedly and require managing state between calls, a custom fake implementation is more readable and maintainable than a generic mocking framework. The proposed `FakeChatModel` class allows developers to script specific responses, simulate errors, and manage call history, enabling more robust testing of retry logic, agent loops, and token limits. AI

IMPACT Provides a practical technique for developers to improve the reliability of applications that integrate with LLM clients.

RANK_REASON The article provides a technical how-to guide for software developers on a specific testing technique for LLM clients.

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