Prompt and configuration operations · Lesson 4 of 14 · 7 min

Validate prompt variables

Prompt-variable validation catches missing substitutions before inference. An unresolved placeholder is a configuration defect, not useful context for the model to guess.

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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-004.json. Separate the recorded facts from your assumptions.
Hint & worked example
Prompt-variable validation catches missing substitutions before inference. An unresolved placeholder is a configuration defect, not useful context for the model to guess.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-prompts-config-004.json
```
Common mistake to avoid: Ask the model to infer the missing private context.
2
Isolate the deciding signal
Select the missing field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Supply the missing context or return a clear validation error before inference.
A successful JSON query isolates missing.
```
jq .missing /opt/ai-lab/cases/aiops-prompts-config-004.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: supply-context, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-prompts-config-004-decision.txt.
Hint & worked example
Supply the missing context or return a clear validation error before inference.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'supply-context' > /home/learner/lab/aiops-prompts-config-004-decision.txt
```
Common mistake to avoid: Ask the model to infer the missing private context.

Understand the pattern

Prompt-variable validation catches missing substitutions before inference. An unresolved placeholder is a configuration defect, not useful context for the model to guess.

Interpret the evidence

Supply the missing context or return a clear validation error before inference.

A tempting mistake

Ask the model to infer the missing private context.

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-004.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "required": [
    "question",
    "context"
  ],
  "provided": [
    "question"
  ],
  "missing": [
    "context"
  ]
}

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

$ jq .missing /opt/ai-lab/cases/aiops-prompts-config-004.json

[
  "context"
]

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

$ printf '%s\n' 'supply-context' > /home/learner/lab/aiops-prompts-config-004-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 “supply-context” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Validate prompt variables” justify?Ask the model to infer the missing private context.Make the change now and gather the missing evidence afterward.Supply the missing context or return a clear validation error before inference.

Apply it elsewhere

A colleague concludes: “Ask the model to infer the missing private context.” 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.

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Case methodology: evidence, limits, decision