Your AI feature worked yesterday. Today, a prompt change, model upgrade, or retrieval tweak quietly reduced answer quality—and every traditional test still passes.

Unit and integration tests can verify code paths, API contracts, and deterministic behavior. They cannot tell you whether an answer is grounded, whether important information was missed, or whether a response became less useful after a change. That requires application-specific evals.

Using an anonymized internal knowledge assistant, this session starts with a small application that appears to work. We will expose three realistic failure modes: unsupported claims, missed key information, and brittle behavior after a change. Then we will turn those failures into a lightweight eval suite.

The demo covers a compact golden dataset, pass/fail checks, rubric-based scoring, pairwise comparison, and a regression workflow for prompt, model, and retrieval changes. We will also trace failed cases to determine whether the problem came from retrieval, generation, or the surrounding system design.

Attendees will leave able to distinguish benchmarks from application evals, create a small but useful dataset, choose an appropriate scoring method, and add repeatable quality checks to an existing delivery workflow.

You cannot make every response deterministic. You can make quality visible before your users find the regression.

Audience: Software engineers and technical leads building LLM or RAG features.
Format: 45–60-minute conference talk with live demonstrations. This reusable profile is not the 120-minute workshop version.
Demo: An anonymized knowledge assistant, three failure modes, a golden dataset, scoring, and a regression workflow.
Evidence: Accepted for The Commit Your Code Conference 2026.
Materials: Talk-specific repository, slides, and recording are not yet published.
Vendor scope: Vendor-neutral testing patterns; implementation examples use C#/.NET.