Inference requests and APIs · Lesson 2 of 16 · 9 min

List served models

A served-model list describes identifiers the gateway accepts. Use the exact served identifier rather than a local filename or an unrelated registry name.

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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-002.json. Separate the recorded facts from your assumptions.
Hint & worked example
A served-model list describes identifiers the gateway accepts. Use the exact served identifier rather than a local filename or an unrelated registry name.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-002.json
```
Common mistake to avoid: Assume every artifact in storage is a served model identifier.
2
Isolate the deciding signal
Select the served_models field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Use assistant-stable or deliberately deploy the candidate before requesting it.
A successful JSON query isolates served_models.
```
jq .served_models /opt/ai-lab/cases/aiops-inference-apis-002.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: use-served-id, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-002-decision.txt.
Hint & worked example
Use assistant-stable or deliberately deploy the candidate before requesting it.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'use-served-id' > /home/learner/lab/aiops-inference-apis-002-decision.txt
```
Common mistake to avoid: Assume every artifact in storage is a served model identifier.

Understand the pattern

A served-model list describes identifiers the gateway accepts. Use the exact served identifier rather than a local filename or an unrelated registry name.

Interpret the evidence

Use assistant-stable or deliberately deploy the candidate before requesting it.

A tempting mistake

Assume every artifact in storage is a served model identifier.

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-002.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "served_models": [
    "assistant-stable"
  ],
  "requested_model": "assistant-candidate"
}

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

$ jq .served_models /opt/ai-lab/cases/aiops-inference-apis-002.json

[
  "assistant-stable"
]

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

$ printf '%s\n' 'use-served-id' > /home/learner/lab/aiops-inference-apis-002-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 “use-served-id” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “List served models” justify?Use assistant-stable or deliberately deploy the candidate before requesting it.Assume every artifact in storage is a served model identifier.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Assume every artifact in storage is a served model identifier.” 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: Read a request envelope Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision