Evaluations and datasets · Lesson 9 of 18 · 6 min

Set a release threshold

A release threshold should be chosen before looking at candidate results. Include critical failure bounds, not only a single average target.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-evals-datasets-009.json. Separate the recorded facts from your assumptions.
Hint & worked example
A release threshold should be chosen before looking at candidate results. Include critical failure bounds, not only a single average target.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-009.json
```
Common mistake to avoid: Lower the gate after seeing the score and call the original goal achieved.
2
Isolate the deciding signal
Select the candidate_pass_rate field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Hold the candidate because 0.93 is below the predefined 0.95 gate.
A successful JSON query isolates candidate_pass_rate.
```
jq .candidate_pass_rate /opt/ai-lab/cases/aiops-evals-datasets-009.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: hold-below-threshold, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-009-decision.txt.
Hint & worked example
Hold the candidate because 0.93 is below the predefined 0.95 gate.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'hold-below-threshold' > /home/learner/lab/aiops-evals-datasets-009-decision.txt
```
Common mistake to avoid: Lower the gate after seeing the score and call the original goal achieved.

Understand the pattern

A release threshold should be chosen before looking at candidate results. Include critical failure bounds, not only a single average target.

Interpret the evidence

Hold the candidate because 0.93 is below the predefined 0.95 gate.

A tempting mistake

Lower the gate after seeing the score and call the original goal achieved.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-evals-datasets-009.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "required_pass_rate": 0.95,
  "candidate_pass_rate": 0.93,
  "critical_failures": 0
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .candidate_pass_rate /opt/ai-lab/cases/aiops-evals-datasets-009.json

0.93

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'hold-below-threshold' > /home/learner/lab/aiops-evals-datasets-009-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “hold-below-threshold” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Set a release threshold” justify?Lower the gate after seeing the score and call the original goal achieved.Make the change now and gather the missing evidence afterward.Hold the candidate because 0.93 is below the predefined 0.95 gate.

Apply it elsewhere

A colleague concludes: “Lower the gate after seeing the score and call the original goal achieved.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Review metric tradeoffs Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision