AI & Development
Testing AI applications is fundamentally different from testing traditional software. Learn the strategies and tools that make LLM application testing
Testing an LLM application requires a fundamentally different approach than testing traditional software. A traditional function given the same input always returns the same output - you can write an assertion and it either passes or fails. An LLM given the same input at temperature 0 returns the same output, but that output changes when the model is updated, when the system prompt is modified, or when the context changes. Testing strategies that work for traditional software will fail for AI applications.
The first step is being clear about what you are testing. In an LLM application, there are several distinct layers: the model itself, the prompt (system prompt plus any templating), the retrieval pipeline (for RAG applications), the parsing and validation of model outputs, and the downstream logic that uses those outputs. Each layer has different testing needs.
Testing the model itself is typically not practical or necessary - you are using a model API, not developing a model. Testing your prompt, your retrieval pipeline, and your parsing logic is where you focus. Changes to any of these can degrade application quality without any change to the underlying model.
The foundation of AI testing is a curated evaluation set: a collection of inputs with expected outputs or evaluation criteria. For classification tasks, this is straightforward - inputs with known correct labels. For open-ended tasks like summarization or question answering, the expected output is either a reference answer for comparison or a rubric that defines what a good answer looks like.
A good evaluation set includes representative examples from the normal distribution of your use case, edge cases that you have already encountered in testing or production, and adversarial examples that are designed to break the application. The adversarial examples are especially valuable - they test the robustness of your prompt and pipeline against unusual inputs that real users will eventually send.
Not all testing has to be fuzzy. Many properties of LLM outputs can be checked deterministically. Does the output contain the required fields? Is it valid JSON? Is the category one of the expected categories? Does it include links that resolve to valid URLs? Does it avoid mentioning the competitor's name? These checks are fast, cheap, and reliable - they should run on every output before it leaves your system.
Deterministic checks catch a large fraction of failures quickly and give you a binary pass/fail signal that is suitable for CI pipelines. If a prompt change causes the model to start outputting invalid JSON, a deterministic check catches it immediately. Reserve the more expensive evaluation approaches for quality dimensions that cannot be checked deterministically.
For quality dimensions that require judgment - is this response helpful? is it accurate? does it match the required tone? - LLM-as-judge evaluation is the practical approach at scale. A capable evaluation model (separate from the model being evaluated) scores each output against a rubric. This is not as reliable as human evaluation but is orders of magnitude faster and cheaper, making it practical for continuous evaluation on every deployment.
Well-designed rubrics are specific and actionable: "Rate the factual accuracy of this response on a scale of 1-5, where 5 means every claim in the response is supported by the provided source documents and 1 means multiple claims contradict or are absent from the source documents." Vague rubrics produce inconsistent scores that are not useful for making decisions.
Model providers update their models, and updates can change behavior in ways that break your application. Running your full evaluation set after a model update and comparing scores to the previous baseline is the only reliable way to catch regressions before they reach production. Treat model updates the same way you treat dependency updates in traditional software: validate before deploying.
For applications where consistency is more important than absolute quality, pinning to a specific model version rather than "latest" gives you control over when changes are introduced. Most major providers offer model version pinning. The trade-off is that you also miss any improvements the provider ships - a deliberate choice to make on a per-application basis.
Pre-deployment testing is necessary but not sufficient. AI applications fail in production in ways that do not appear in test sets - unusual user inputs, edge cases you did not anticipate, drift in user behavior over time. Logging all inputs and outputs, computing quality metrics on production traffic (using automated evaluation where feasible), and sampling for human review creates the feedback loop that lets you improve the application continuously. Pre-deployment testing and production monitoring together constitute a complete testing strategy for AI applications.