Quality Explained: Concepts, Workflows and Tools
Automated tests that grade LLM outputs against expected behavior. Evals are the unit tests of AI systems. 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.
Frequently asked questions
- What is Evals?
- Evals are the unit tests of AI systems. Each eval is an input, an expected property (exact match, LLM-judge score, latency budget), and a pass/fail. Ship without evals and you fly blind.
- What is an example of Evals?
- An eval suite runs 200 real support questions nightly; a regression alerts if grounded-answer accuracy drops below 92%.
- Why does Evals matter for AI and automation?
- Automated tests that grade LLM outputs against expected behavior. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is LLM-as-Judge?
- LLM-as-judge automates eval scoring: give the judge the input, the output, and a rubric, and get a score. Cheap alternative to human labeling — with known biases (length, position) to control for.
- What is an example of LLM-as-Judge?
- GPT-4 grades 500 summaries against a rubric: relevance 1-5, faithfulness 1-5.
- Why does LLM-as-Judge matter for AI and automation?
- Using a strong model to grade another model's output. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is AI Testing?
- Testing with AI means generating unit, integration and edge-case tests from code and specs — cheap coverage, but it must be reviewed or it just encodes current behaviour as correct. Testing AI means grading probabilistic output: assertion-based checks for structure, rubric or model-graded checks for quality, and regression suites of real failures. Snapshot tests break immediately on model output; behavioural assertions survive.
- What is an example of AI Testing?
- An extraction feature is tested with 40 stored documents asserting schema validity and field-level accuracy above 95%, not exact string equality.