Inference requests and APIs · Lesson 3 of 16 · 5 min

Read a request envelope

A request envelope combines model selection, messages and generation constraints. Invalid field types should be corrected at the contract boundary before retrying.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-inference-apis-003.json. Separate the recorded facts from your assumptions.
Hint & worked example
A request envelope combines model selection, messages and generation constraints. Invalid field types should be corrected at the contract boundary before retrying.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-003.json
```
Common mistake to avoid: Retry the same invalid string until the type requirement changes.
2
Isolate the deciding signal
Select the expected_messages_type field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Encode messages as the expected array instead of a plain string.
A successful JSON query isolates expected_messages_type.
```
jq .expected_messages_type /opt/ai-lab/cases/aiops-inference-apis-003.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: fix-messages-array, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-003-decision.txt.
Hint & worked example
Encode messages as the expected array instead of a plain string.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'fix-messages-array' > /home/learner/lab/aiops-inference-apis-003-decision.txt
```
Common mistake to avoid: Retry the same invalid string until the type requirement changes.

Understand the pattern

A request envelope combines model selection, messages and generation constraints. Invalid field types should be corrected at the contract boundary before retrying.

Interpret the evidence

Encode messages as the expected array instead of a plain string.

A tempting mistake

Retry the same invalid string until the type requirement changes.

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-003.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "model": "assistant-stable",
  "messages": "hello",
  "expected_messages_type": "array"
}

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

$ jq .expected_messages_type /opt/ai-lab/cases/aiops-inference-apis-003.json

"array"

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

$ printf '%s\n' 'fix-messages-array' > /home/learner/lab/aiops-inference-apis-003-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 “fix-messages-array” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read a request envelope” justify?Retry the same invalid string until the type requirement changes.Make the change now and gather the missing evidence afterward.Encode messages as the expected array instead of a plain string.

Apply it elsewhere

A colleague concludes: “Retry the same invalid string until the type requirement changes.” 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: Read a response envelope Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision