Prompt and configuration operations · Lesson 12 of 14 · 5 min

Write a change note

A change note records why a revision exists, what evidence supports it and how to reverse it. Avoid vague success claims that cannot be checked against an evaluation.

Open the lab Course map

Mastery 0/3Run itExplain itApply it

learner@clairos:~ $

↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-prompts-config-012.json. Separate the recorded facts from your assumptions.
Hint & worked example
A change note records why a revision exists, what evidence supports it and how to reverse it. Avoid vague success claims that cannot be checked against an evaluation.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-prompts-config-012.json
```
Common mistake to avoid: Write “better AI” without identifying what was measured.
2
Isolate the deciding signal
Select the evaluation field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Reference eval-23 and the p7 rollback in the change note.
A successful JSON query isolates evaluation.
```
jq .evaluation /opt/ai-lab/cases/aiops-prompts-config-012.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: record-eval-23, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-prompts-config-012-decision.txt.
Hint & worked example
Reference eval-23 and the p7 rollback in the change note.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'record-eval-23' > /home/learner/lab/aiops-prompts-config-012-decision.txt
```
Common mistake to avoid: Write “better AI” without identifying what was measured.

Understand the pattern

A change note records why a revision exists, what evidence supports it and how to reverse it. Avoid vague success claims that cannot be checked against an evaluation.

Interpret the evidence

Reference eval-23 and the p7 rollback in the change note.

A tempting mistake

Write “better AI” without identifying what was measured.

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-prompts-config-012.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "change": "p7 to p8",
  "reason": "reduce unsupported claims",
  "evaluation": "eval-23",
  "rollback": "p7"
}

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

$ jq .evaluation /opt/ai-lab/cases/aiops-prompts-config-012.json

"eval-23"

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

$ printf '%s\n' 'record-eval-23' > /home/learner/lab/aiops-prompts-config-012-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 “record-eval-23” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Write a change note” justify?Reference eval-23 and the p7 rollback in the change note.Write “better AI” without identifying what was measured.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Write “better AI” without identifying what was measured.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Review prompt observability Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision