AI systems foundations · Lesson 7 of 15 · 6 min

Identify deterministic components

Application arithmetic, validation and routing can be deterministic even when text generation is variable. Keep hard constraints in code instead of trusting generated prose to enforce them.

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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-systems-foundations-007.json. Separate the recorded facts from your assumptions.
Hint & worked example
Application arithmetic, validation and routing can be deterministic even when text generation is variable. Keep hard constraints in code instead of trusting generated prose to enforce them.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-systems-foundations-007.json
```
Common mistake to avoid: Let the model silently replace the calculated invoice total.
2
Isolate the deciding signal
Select the invoice_total_source field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Keep the integer calculation authoritative and validate the generated summary against it.
A successful JSON query isolates invoice_total_source.
```
jq .invoice_total_source /opt/ai-lab/cases/aiops-systems-foundations-007.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: keep-code-authority, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-systems-foundations-007-decision.txt.
Hint & worked example
Keep the integer calculation authoritative and validate the generated summary against it.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'keep-code-authority' > /home/learner/lab/aiops-systems-foundations-007-decision.txt
```
Common mistake to avoid: Let the model silently replace the calculated invoice total.

Understand the pattern

Application arithmetic, validation and routing can be deterministic even when text generation is variable. Keep hard constraints in code instead of trusting generated prose to enforce them.

Interpret the evidence

Keep the integer calculation authoritative and validate the generated summary against it.

A tempting mistake

Let the model silently replace the calculated invoice total.

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-007.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "invoice_total_source": "integer_cents_calculation",
  "summary_source": "model",
  "validation": "exact_total_match"
}

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

$ jq .invoice_total_source /opt/ai-lab/cases/aiops-systems-foundations-007.json

"integer_cents_calculation"

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

$ printf '%s\n' 'keep-code-authority' > /home/learner/lab/aiops-systems-foundations-007-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 “keep-code-authority” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Identify deterministic components” justify?Let the model silently replace the calculated invoice total.Make the change now and gather the missing evidence afterward.Keep the integer calculation authoritative and validate the generated summary against it.

Apply it elsewhere

A colleague concludes: “Let the model silently replace the calculated invoice total.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Recognize probabilistic output Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision