Eval-driven AI is a development discipline that writes the evaluation harness before the agent, scores every output against a versioned rubric, and ships only when the metrics are green. It is the AI equivalent of writing integration tests before a feature, adapted to outputs that aren't binary.
The three layers of an eval harness.
- Fixtures. A curated dataset of real inputs, labeled with what a good output looks like. 50 to 200 examples to start, sourced from real traffic.
- Rubric. The written definition of “good” for each input class. Sometimes deterministic, sometimes LLM-graded, occasionally human-graded. Versioned alongside the prompts.
- Regression suite. A baseline number for every metric, stored on every release. New releases ship only when no metric regresses past a defined tolerance.
Why eval-driven AI is the only AI that ships.
Without evals, AI projects regress silently. A model swap, a prompt change, a retrieval refactor, all can degrade quality in ways nobody notices until a customer complains. The eval harness is the only thing in the project that survives those changes unchanged, and the only objective answer to “is this better than last week?”
“An AI system without evals is a vibe. A vibe is not a product.”
The field rule.
If the engineering lead cannot show you the eval script and the scoreboard from last week's release, the AI system in question is not shippable. It is just running. Every Morvion AI engagement starts here, before any prompt or retrieval pipeline is written.
Frequently asked.
- What is eval-driven AI?
- Eval-driven AI is a development discipline that writes the evaluation harness before the agent, scores every output against a versioned rubric, and ships only on green metrics. It is the AI equivalent of writing integration tests before a feature.
- What is an AI eval harness?
- An AI eval harness is a repeatable test suite for an AI system. It runs a fixed set of inputs through the current model and prompt pipeline, scores each output against a written rubric, and produces metrics that can be compared across releases. It is the AI equivalent of an integration test, adapted to non-deterministic outputs.
- Why build the eval before the agent?
- Because if the eval is built second, it gets shaped around whatever the current agent happens to do well. The harness becomes a flatterer. Built first, the agent has a target to optimise against instead of a vibe to chase.
- How many fixtures do you need to start?
- 50 to 200 examples is enough to start, sourced from real traffic rather than imagined personas. If you cannot produce a fixture set, you do not understand the problem yet, and any AI system you build will reflect that gap.