Building With Quality: A Practical System
Automated tests that grade LLM outputs against expected behavior. This guide pulls together everything on Onexial tagged quality — 7 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 Quality
Before wiring anything together, the vocabulary has to be precise. These 4 definitions cover the terms that show up in almost every Quality discussion — each one links to a full entry with an example and its own connections inside the graph.
Evals
Automated tests that grade LLM outputs against expected behavior.
LLM-as-Judge
Using a strong model to grade another model's output.
AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
Workflows: how Quality runs end to end
Concepts only matter once they become a repeatable process. Below are 2 documented workflows that apply Quality to a concrete problem, with the steps, the tools involved and the variations worth testing.
Build an AI Code Review Loop
Catch what agents get wrong before a human reads the PR.
Build a Test Suite for a Non-Deterministic AI Feature
Grade probabilistic output without brittle snapshot tests.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a Quality workflow with minimal editing, including the context it expects and an example output.