Cost, capacity, and reliability · Lesson 4 of 14 · 7 min

Inspect cache effectiveness

Cache effectiveness depends on hit rate, eligible work and correctness. A high hit rate is harmful when responses come from stale or improperly shared entries.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-cost-reliability-004.json. Separate the recorded facts from your assumptions.
Hint & worked example
Cache effectiveness depends on hit rate, eligible work and correctness. A high hit rate is harmful when responses come from stale or improperly shared entries.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-cost-reliability-004.json
```
Common mistake to avoid: An 80 percent hit rate proves cache correctness.
2
Isolate the deciding signal
Select the stale_hits field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Investigate the stale hits rather than celebrating the hit rate alone.
A successful JSON query isolates stale_hits.
```
jq .stale_hits /opt/ai-lab/cases/aiops-cost-reliability-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: repair-stale-cache, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-cost-reliability-004-decision.txt.
Hint & worked example
Investigate the stale hits rather than celebrating the hit rate alone.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'repair-stale-cache' > /home/learner/lab/aiops-cost-reliability-004-decision.txt
```
Common mistake to avoid: An 80 percent hit rate proves cache correctness.

Understand the pattern

Cache effectiveness depends on hit rate, eligible work and correctness. A high hit rate is harmful when responses come from stale or improperly shared entries.

Interpret the evidence

Investigate the stale hits rather than celebrating the hit rate alone.

A tempting mistake

An 80 percent hit rate proves cache correctness.

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-cost-reliability-004.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "cache_hit_rate": 0.8,
  "stale_hits": 12,
  "tenant_keyed": true
}

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

$ jq .stale_hits /opt/ai-lab/cases/aiops-cost-reliability-004.json

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

$ printf '%s\n' 'repair-stale-cache' > /home/learner/lab/aiops-cost-reliability-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 “repair-stale-cache” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect cache effectiveness” justify?Investigate the stale hits rather than celebrating the hit rate alone.An 80 percent hit rate proves cache correctness.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “An 80 percent hit rate proves cache correctness.” 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 concurrency limits Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision