Evals for Internal AI Systems
How to test RAG systems, agent workflows, and internal AI tools before teams depend on them.
Direct answer
Evals are repeatable tests that measure whether an AI system gives correct, grounded, useful, and safe outputs for the business situations it will actually face.
What gets tested
The useful unit of evaluation is not the model in isolation. It is the behavior of the full workflow: retrieval, prompt, model, tool call, output format, review step, and final business action.
A good eval set starts with real examples from the business, not synthetic prompts that only test happy paths.
- Golden examples from real documents, tickets, calls, or workflow history.
- Retrieval relevance and citation checks.
- Groundedness and hallucination checks.
- Tool-call correctness.
- Regression tests after prompt, data, or model changes.
What is a golden dataset?
A golden dataset is a curated set of representative inputs and expected outputs used to test whether an AI system still behaves correctly as the system changes.
Can an LLM judge another LLM?
Yes, for some quality checks, but LLM-as-judge should be calibrated against human review and used carefully for high-stakes decisions.
When should evals be added?
Before a workflow is used by a team. Evals are much harder to retrofit after people already rely on the system.
Want to know what a build like this would involve?
The first deliverable of every engagement is a scoped build plan — integration map, what to automate first, and a fixed number.
Get a build plan