Inference requests and APIs · Lesson 4 of 16 · 6 min

Read a response envelope

A response envelope contains output plus metadata such as completion status and usage. Inspect the finish reason before treating partial output as a complete answer.

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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-inference-apis-004.json. Separate the recorded facts from your assumptions.
Hint & worked example
A response envelope contains output plus metadata such as completion status and usage. Inspect the finish reason before treating partial output as a complete answer.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-004.json
```
Common mistake to avoid: Nonempty output always means the answer is complete.
2
Isolate the deciding signal
Select the finish_reason field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Treat length termination as potential truncation and review the output budget.
A successful JSON query isolates finish_reason.
```
jq .finish_reason /opt/ai-lab/cases/aiops-inference-apis-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: review-truncation, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-004-decision.txt.
Hint & worked example
Treat length termination as potential truncation and review the output budget.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'review-truncation' > /home/learner/lab/aiops-inference-apis-004-decision.txt
```
Common mistake to avoid: Nonempty output always means the answer is complete.

Understand the pattern

A response envelope contains output plus metadata such as completion status and usage. Inspect the finish reason before treating partial output as a complete answer.

Interpret the evidence

Treat length termination as potential truncation and review the output budget.

A tempting mistake

Nonempty output always means the answer is complete.

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-inference-apis-004.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "finish_reason": "length",
  "output_present": true,
  "complete_answer": false
}

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

$ jq .finish_reason /opt/ai-lab/cases/aiops-inference-apis-004.json

"length"

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

$ printf '%s\n' 'review-truncation' > /home/learner/lab/aiops-inference-apis-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 “review-truncation” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read a response envelope” justify?Make the change now and gather the missing evidence afterward.Treat length termination as potential truncation and review the output budget.Nonempty output always means the answer is complete.

Apply it elsewhere

A colleague concludes: “Nonempty output always means the answer is complete.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

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Case methodology: evidence, limits, decision