Traditional software testing asks whether a function returned the expected output. LLM systems force a harder question: did the system produce a useful, safe, grounded, and policy-compliant response for this workflow?
That is why evaluation is becoming the new unit test for AI applications.
A prompt that works today may degrade tomorrow. A model update may improve one use case and break another. A retrieval pipeline may return the wrong document. A user may phrase a request in a way the team never anticipated. The application may still be “working” from an uptime perspective while output quality quietly declines.
Why traditional QA is not enough
Traditional QA is not enough because LLM failures are often subtle. The page loads. The button works. The API returns a response. But the answer may be incomplete, ungrounded, too expensive, too slow, or outside policy. Without evaluation, the team is left with demos, anecdotes, and user complaints.
A useful evaluation program begins with the workflow, not the model. A support copilot, a compliance assistant, a sales research tool, and a clinical summarizer all require different evaluation criteria. The team should define what a good answer looks like in context. Accuracy may matter most in one workflow. Completeness may matter more in another. In regulated environments, grounding and escalation behavior may be more important than fluency.
The golden dataset
The core asset is a golden dataset. This dataset should include common cases, edge cases, ambiguous cases, policy-restricted cases, missing-information cases, and examples from real workflow logs where possible. It should grow over time. Every meaningful production failure should become a future test case.
Evaluate the whole system
Evaluation should measure the full system. Was the right data retrieved? Was the user allowed to access that data? Was the answer grounded in sources? Did the output follow the required format? Did the system escalate when uncertain? Did it avoid restricted content? Did the workflow save time without increasing risk? These are product and operational questions, not just model questions.
Some evaluation can be automated. Format checks, citation presence, retrieval quality, latency, cost, and policy rules can often be measured systematically. Other evaluation requires human review, especially in complex domains. The goal is not to automate every judgment. The goal is to create a repeatable loop where quality becomes visible.
Without an evaluation loop, each AI release becomes an act of faith.
Run evaluations before releases
Evaluation should run before releases. If prompts change, models change, retrieval logic changes, documents change, tools are added, or permissions change, the evaluation harness should tell the team what improved and what regressed.
The organizations that scale AI successfully will not be the ones with the flashiest demos. They will be the ones that build disciplined evaluation into the delivery process. Evaluation turns AI from a novelty into software engineering.
How Meridyn Labs helps
Meridyn Labs builds LLM applications with evaluation harnesses, retrieval testing, observability, cost tracking, governance, and release discipline built in from the beginning.