Prompt and configuration operations · Lesson 7 of 14 · 5 min

Locate a prompt owner

Ownership identifies who can explain, approve and roll back a prompt change. A named owner is useful only when the escalation and change process are recorded.

Open the lab Course map

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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-prompts-config-007.json. Separate the recorded facts from your assumptions.
Hint & worked example
Ownership identifies who can explain, approve and roll back a prompt change. A named owner is useful only when the escalation and change process are recorded.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-prompts-config-007.json
```
Common mistake to avoid: Assume every operator may change production prompts without review.
2
Isolate the deciding signal
Select the owner field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Route the prompt question to support-ops through the documented review queue.
A successful JSON query isolates owner.
```
jq .owner /opt/ai-lab/cases/aiops-prompts-config-007.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: contact-support-ops, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-prompts-config-007-decision.txt.
Hint & worked example
Route the prompt question to support-ops through the documented review queue.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'contact-support-ops' > /home/learner/lab/aiops-prompts-config-007-decision.txt
```
Common mistake to avoid: Assume every operator may change production prompts without review.

Understand the pattern

Ownership identifies who can explain, approve and roll back a prompt change. A named owner is useful only when the escalation and change process are recorded.

Interpret the evidence

Route the prompt question to support-ops through the documented review queue.

A tempting mistake

Assume every operator may change production prompts without review.

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-007.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "prompt": "p7",
  "owner": "support-ops",
  "escalation": "prompt-review queue"
}

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

$ jq .owner /opt/ai-lab/cases/aiops-prompts-config-007.json

"support-ops"

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

$ printf '%s\n' 'contact-support-ops' > /home/learner/lab/aiops-prompts-config-007-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 “contact-support-ops” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Locate a prompt owner” justify?Assume every operator may change production prompts without review.Make the change now and gather the missing evidence afterward.Route the prompt question to support-ops through the documented review queue.

Apply it elsewhere

A colleague concludes: “Assume every operator may change production prompts without review.” 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: Inspect configuration precedence Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision