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For dependable AI evaluation CI, track exactly which evaluation inputs produced each run, and separately tell CTest how many declared resources tests may use in parallel. A prompt hash is a project-defined identity key, not a guarantee of identical model output; CTest resource slots coordinate declared capacity, not a universal process-RSS limit.
Lock 1: Give each evaluation a defined identity
A hash is useful only if you define exactly what is hashed. Hashing just a prompt template can miss changes to rendered variables, instructions, tools, or other context that affected the evaluation. Hashing a fully rendered message captures more of the tested input, but still does not identify the dataset, grader, model configuration, or harness that produced the result.
OpenAI’s Evals documentation describes evaluations in terms of criteria, data-source configuration, templated messages, graders, and runs; it also gives prompt-version as an example metadata value. Treat those fields as a practical guide to what a run record may need, not as a prescribed hashing standard. The documentation does not define a canonical prompt-hash algorithm.
Choose and record the hash scope
Decide whether the identity covers the source template or the fully rendered messages. If it covers rendered input, specify how variable values are represented. Also decide whether to include system and developer instructions, tool schemas, and other context that changes what the model receives. State the policy in the manifest so that two runs with different effective inputs do not appear identical by accident.
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Keep the inputs inspectable
Store a versioned manifest alongside the hash. A stable serialization with a schema version, stable field ordering, and explicit encodings makes the hash reproducible; retain the manifest itself so a person can inspect what the digest represents. For example, a project might record fields like these:
{
"schema_version": 1,
"prompt_identity": "rendered-messages",
"prompt_version": "support-eval-v3",
"dataset_version": "cases-2026-04",
"grader_version": "rubric-v2",
"model_snapshot": "pinned-snapshot",
"model_parameters": {},
"harness_revision": "git-commit"
}These field names and values are illustrative, not an OpenAI or CMake format. Use the real versions and configuration for your project, and hash the documented canonical representation rather than an informal subset of it.
Keep prompt identity separate from output reproducibility
The digest identifies the inputs covered by your policy; it does not promise that the model will return the same output on every run. OpenAI notes that behavior can vary between model snapshots and recommends pinned model versions and application evaluations for consistency. Record the model snapshot and parameters separately, retain dataset and grader versions, and rerun the evaluation when relevant model or evaluation inputs change.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsLock 2: Schedule declared test resources with CTest
CTest resource allocation is a cooperative scheduler. A resource specification describes available capacity on the runner, while each test’s RESOURCE_GROUPS requirement declares the slots it needs. When the feature is used, CTest avoids scheduling more allocated slots than the configured capacity.
Make the capacity declaration match the runner
Provide a resource specification for the machine or generate one that reflects the runner’s available capacity, then pass it to the CTest invocation. Add RESOURCE_GROUPS requirements to tests that need those resources. CTest does not discover GPU capacity for you: the project must declare it. A test requesting more slots than are available is reported as not run.
Make tests use the allocation
Tests must read and honor the allocated-resource information CTest provides through the environment. Do not let a test independently assume it owns a GPU or other scarce resource simply because it requested one. Also verify that the CI invocation actually supplies the resource specification: without it, the test harness must not assume CTest allocation is active.
This mechanism limits only the resources represented in the specification and requested by tests. It is useful for preventing declared test workloads from oversubscribing declared slots; it does not impose a general memory ceiling on each process.
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Peak resident set size (RSS) is a memory measurement, while CTest resource allocation schedules declared abstract slots. CTest separately documents a memory-check step that runs tests through a memory checker, but neither facility is documented as a universal, cross-platform peak-RSS limiter.
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If CI needs a hard memory limit, select and verify a mechanism provided by the target runner, operating system, or container environment. First define whether the limit is per process or for aggregate job memory, then verify how the environment accounts for container memory. Do not infer an RSS limit from a resource-slot declaration or from running a test under a memory checker.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Record enough to explain each CI result
Keep the evaluation manifest and hash with the run’s model snapshot and parameters, dataset and grader versions, harness revision, build configuration, resource specification, and test output. This connects the evaluation identity to the environment that ran it and makes a changed result easier to diagnose. CTest’s dashboard workflow supports configure, build, and test reporting; it does not dictate how a project retains CI artifacts.
In practice, the two controls answer different questions: the manifest and hash identify the evaluation inputs, while CTest allocation coordinates declared parallel resource use. A separate runner-level mechanism is needed when the requirement is a hard RSS ceiling.
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