Inference requests and APIs · Lesson 5 of 16 · 7 min

Inspect token usage

Usage accounts for input and generated tokens separately. Compare the sum with the recorded total and keep the tokenizer/model context with any estimate.

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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-005.json. Separate the recorded facts from your assumptions.
Hint & worked example
Usage accounts for input and generated tokens separately. Compare the sum with the recorded total and keep the tokenizer/model context with any estimate.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-005.json
```
Common mistake to avoid: Count only generated output as total token usage.
2
Isolate the deciding signal
Select the total_tokens field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Record 520 total tokens, with 400 input and 120 output.
A successful JSON query isolates total_tokens.
```
jq .total_tokens /opt/ai-lab/cases/aiops-inference-apis-005.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: record-520-tokens, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-005-decision.txt.
Hint & worked example
Record 520 total tokens, with 400 input and 120 output.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'record-520-tokens' > /home/learner/lab/aiops-inference-apis-005-decision.txt
```
Common mistake to avoid: Count only generated output as total token usage.

Understand the pattern

Usage accounts for input and generated tokens separately. Compare the sum with the recorded total and keep the tokenizer/model context with any estimate.

Interpret the evidence

Record 520 total tokens, with 400 input and 120 output.

A tempting mistake

Count only generated output as total token usage.

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-005.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "input_tokens": 400,
  "output_tokens": 120,
  "total_tokens": 520
}

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

$ jq .total_tokens /opt/ai-lab/cases/aiops-inference-apis-005.json

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

$ printf '%s\n' 'record-520-tokens' > /home/learner/lab/aiops-inference-apis-005-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 “record-520-tokens” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect token usage” justify?Record 520 total tokens, with 400 input and 120 output.Count only generated output as total token usage.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Count only generated output as total token usage.” 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: Distinguish client and server errors Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision