Testing: The Complete Guide
Reproducible test suites that measure LLM output quality across model, prompt and code changes. This guide pulls together everything on Onexial tagged testing — 5 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Testing
Before wiring anything together, the vocabulary has to be precise. These 2 definitions cover the terms that show up in almost every Testing discussion — each one links to a full entry with an example and its own connections inside the graph.
AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
Workflows: how Testing runs end to end
Concepts only matter once they become a repeatable process. Below are 1 documented workflows that apply Testing to a concrete problem, with the steps, the tools involved and the variations worth testing.
The Testing tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 1 stacks show which combinations hold up in production for Testing, and what each layer is actually responsible for.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a Testing workflow with minimal editing, including the context it expects and an example output.