AI systems foundations · Lesson 14 of 15 · 8 min

Write a system baseline

A reproducible baseline records artifact, configuration, dataset and environment versions together. Keeping only an average score makes later comparisons ambiguous.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-systems-foundations-014.json. Separate the recorded facts from your assumptions.
Hint & worked example
A reproducible baseline records artifact, configuration, dataset and environment versions together. Keeping only an average score makes later comparisons ambiguous.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-systems-foundations-014.json
```
Common mistake to avoid: Keep the score but discard the version identifiers.
2
Isolate the deciding signal
Select the dataset field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Record all four versions so the result can be compared with a later run.
A successful JSON query isolates dataset.
```
jq .dataset /opt/ai-lab/cases/aiops-systems-foundations-014.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: capture-versions, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-systems-foundations-014-decision.txt.
Hint & worked example
Record all four versions so the result can be compared with a later run.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'capture-versions' > /home/learner/lab/aiops-systems-foundations-014-decision.txt
```
Common mistake to avoid: Keep the score but discard the version identifiers.

Understand the pattern

A reproducible baseline records artifact, configuration, dataset and environment versions together. Keeping only an average score makes later comparisons ambiguous.

Interpret the evidence

Record all four versions so the result can be compared with a later run.

A tempting mistake

Keep the score but discard the version identifiers.

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-systems-foundations-014.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "model": "m3",
  "prompt": "p7",
  "dataset": "gold-v4",
  "runtime": "r2"
}

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

$ jq .dataset /opt/ai-lab/cases/aiops-systems-foundations-014.json

"gold-v4"

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

$ printf '%s\n' 'capture-versions' > /home/learner/lab/aiops-systems-foundations-014-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 “capture-versions” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Write a system baseline” justify?Make the change now and gather the missing evidence afterward.Record all four versions so the result can be compared with a later run.Keep the score but discard the version identifiers.

Apply it elsewhere

A colleague concludes: “Keep the score but discard the version identifiers.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Foundations review checkpoint Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision