The Developer’s Guide to Testing LLM Apps Before Production
Member-only storyLLMArtificial IntelligenceSoftware DevelopmentSoftware TestingTechnologyThe Developer’s Guide to Testing LLM Apps Before ProductionLearn how to build practical evaluation checks for prompts, RAG pipelines, model changes, and AI product qualityCodeWithYog12 min read·12 hours...
Member-only storyLLMArtificial IntelligenceSoftware DevelopmentSoftware TestingTechnologyThe Developer’s Guide to Testing LLM Apps Before ProductionLearn how to build practical evaluation checks for prompts, RAG pipelines, model changes, and AI product qualityCodeWithYog12 min read·12 hours ago--ListenSharePress enter or click to view image in full sizeMost developers learn testing through clear examples. A normal software test is usually easy to judge. Imagine a function that calculates a discount.calculateDiscount(1000, 10) // 900The function receives the original price and the discount percentage. It returns the final price. If the result is 900, the test passes. If the result is 950, the test fails.That is why traditional testing feels clear. The expected answer is fixed, and the result can be checked directly. That style of testing shaped how many of us think about software quality.Then LLMs changed the situation.You can give the same prompt to a model twice and receive two different answers. Both can sound confident. One can be correct. One can miss the point. One can invent a detail that was never present in the source.This is why LLM evaluation has become part of real software engineering.A good AI feature does not only need a strong prompt. It needs a way to measure quality before users find the mistakes.Source: Generative AI Pub — Published — Category: Image AI