Eval

An Eval system requires a test set with standard answers or scoring criteria, scoring via rule matching, similarity computation, or model-as-judge, and running it after every prompt change, model swap, or retrieval strategy adjustment to use data to judge whether the change is an improvement or a regression.

The third stage of engineering deployment involves details such as wrapping interfaces with FastAPI, streaming output, session state management, logging instrumentation, building an evaluation (Eval) system, test cases covering model randomness, cost and token bill control, online performance tracking, and bad-case recycling.

Eval supports a hill-climbing algorithm, looping repeatedly in Cursor with the /loop command until all scores reach 10.

An Eval is a quantitative check of Agent performance, with common forms including comparison of outcome correctness, judgment of the reasonableness of tool calls, and automatic scoring based on LLM-as-a-Judge.

An eval (Eval) consists of three core components: the input prompt, the execution environment, and the evaluation criteria (Rubric).

Eval is the process of systematically comparing model outputs against expected standards, and common forms include exact match, rule-based assertions, and LLM-as-judge.

Layered testing approaches for Agents include: unit-testing loop termination decisions with fixed inputs, replacing tools with stubs to verify the orchestrator's error handling, and regression-testing policy interception rules with recorded historical tool results.

For early-stage teams, 20–50 precisely labeled samples covering high-frequency core scenarios are already sufficient to provide adequate regression protection.

A common industry practice is to store prompt templates in a code repository, put them through a review process together with code changes, and pair them with automated Eval testing.

An Eval (evaluation benchmark) is a systematic test set and scoring mechanism, where scoring can be rule-based or use the LLM-as-Judge approach.

Due to non-determinism, the same Eval should be run 6 to 10 times to observe the distribution range of accuracy and calculate standard deviation and error.

For deterministically verifiable tasks (such as code generation), traditional methods (compilation, linters, unit tests) should be preferred for evaluation, as they are the fast and reliable gold standard.

Cross-validation using judge agents of different models in Eval prevents agents from detecting they are being tested and changing their behavior, while also offsetting self-preference bias.

Lauren describes the mental model of Eval as "unit tests for agents," where a main coordinator agent formulates a scoring rubric and spawns sub-agents to conduct evaluations.

Evals produce scores that can be hill-climbed, and cross-checking with judge Agents from different models can be used to avoid scoring bias.