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 — 8 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 5 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.
AI Search Ranking Factors
AI search ranking factors are the criteria and signals used by generative AI models to determine the relevance, quality, and trustworthiness of information when formulating responses or generating content.
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?
- Automated tests that grade LLM outputs against expected behavior.
- What should I learn after Evals?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- 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?
- Using a strong model to grade another model's output.
- What should I learn after LLM-as-Judge?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.