Deterministic vs probabilistic testing
// Definition
Traditional software tests are deterministic: same input, same output, pass or fail. AI-backed features are probabilistic: same input can give different outputs, and "correctness" is a distribution rather than a binary. This isn't a small distinction — it breaks most of the assumptions baked into existing test frameworks. Exact-match assertions stop being useful. Flaky-test detection logic flags real model variance as a bug. The unit of measurement shifts from "this test passed" to "this prompt scored 0.87 on average across the eval set, up from 0.83 last week." Senior testers working on AI features spend more time defining what correctness means for a given feature than they do writing assertions.
// Related terms
Large Language Model (LLM)
A neural network trained on massive text datasets to predict the next word in a sequence. Modern LLMs like Claude, GPT-4, and Gemini can answer questions, write code, summarise documents, and follow multi-step instructions — but they don't 'know' anything, they predict plausible continuations from patterns in training data. This is why they sometimes produce confident-sounding falsehoods (hallucinations) and why prompt design matters so much. In QA, LLMs are useful for generating test scaffolding, summarising bug reports, and drafting documentation — but their output always needs human review before it ships.
Hallucination
When an AI model generates output that is fluent, confident, and completely wrong. In QA work this often looks like an LLM inventing a method that doesn't exist on a real API, citing a documentation page that was never written, or producing a test assertion that doesn't actually verify the behaviour described in the prompt. Hallucinations aren't a bug — they're a consequence of how language models work, predicting likely text rather than retrieving facts. The mitigations are: ground the model in real context (paste the actual API spec, not its name), verify generated code by running it, and treat any AI-produced reference (URLs, function names, citations) as untrusted until checked.
Eval harness
Software that runs an LLM-backed system against a dataset of inputs, scores the outputs against criteria (exact match, similarity, LLM-as-judge, custom rubric), and tracks how scores change across model versions, prompts, or code changes. Eval harnesses are to AI features what test runners are to deterministic code: the place CI calls into, the place regressions get caught, the place quality is measured rather than asserted. The 2026 ecosystem has fragmented rather than consolidated — Braintrust is eval-first, Langfuse is prompt-first (acquired by Clickhouse in January), Laminar is built for agent debugging, Arize Phoenix is OpenTelemetry-native. Most teams pick one platform per workflow rather than expecting one tool to cover everything.