Observability and tracing · Lesson 9 of 15 · 7 min

Inspect token metrics

Token metrics help distinguish input growth from output growth. A cost or latency increase can follow a longer context even when the response length remains stable.

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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-observability-tracing-009.json. Separate the recorded facts from your assumptions.
Hint & worked example
Token metrics help distinguish input growth from output growth. A cost or latency increase can follow a longer context even when the response length remains stable.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-observability-tracing-009.json
```
Common mistake to avoid: Only output tokens can affect inference cost or latency.
2
Isolate the deciding signal
Select the input_after field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Inspect the tripled input context instead of blaming unchanged output length.
A successful JSON query isolates input_after.
```
jq .input_after /opt/ai-lab/cases/aiops-observability-tracing-009.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: inspect-input-growth, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-observability-tracing-009-decision.txt.
Hint & worked example
Inspect the tripled input context instead of blaming unchanged output length.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'inspect-input-growth' > /home/learner/lab/aiops-observability-tracing-009-decision.txt
```
Common mistake to avoid: Only output tokens can affect inference cost or latency.

Understand the pattern

Token metrics help distinguish input growth from output growth. A cost or latency increase can follow a longer context even when the response length remains stable.

Interpret the evidence

Inspect the tripled input context instead of blaming unchanged output length.

A tempting mistake

Only output tokens can affect inference cost or latency.

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-observability-tracing-009.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "input_before": 800,
  "input_after": 2400,
  "output_before": 120,
  "output_after": 120
}

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

$ jq .input_after /opt/ai-lab/cases/aiops-observability-tracing-009.json

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

$ printf '%s\n' 'inspect-input-growth' > /home/learner/lab/aiops-observability-tracing-009-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 “inspect-input-growth” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect token metrics” justify?Inspect the tripled input context instead of blaming unchanged output length.Only output tokens can affect inference cost or latency.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Only output tokens can affect inference cost or latency.” 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: Review model saturation Try an incident drill

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Case methodology: evidence, limits, decision