Why deterministic beats LLM-first

Slide 1 — The decision

Leadership

For telemetry-driven process intelligence, the “truth layer” must be deterministic. LLMs are best used as assistive tools (summarize, name, propose), not as authoritative classifiers.

Telemetry is sensitive: privacy, audit, and governance are non-negotiable.
Business decisions require repeatability: the same input must produce the same output.

Slide 2 — Why determinism matters

Risk

Requirement	Why it’s critical	Deterministic-first impact
Auditability	Prove how a label/intent was assigned	Rules/ML versions + logs enable traceability
Replayability	Reprocess historical data consistently	Schema contracts + versioned models ensure stable reruns
Governance	Control changes to taxonomy and rules	Promotion gates prevent silent drift
Cost control	Predictable OPEX for scale	Deterministic pipelines minimize token/latency spikes

Slide 3 — Where LLMs help (safely)

Assist

📝Summarization & namingLow risk

Use LLMs to produce human-readable summaries and label suggestions, but store only after review.

🧪Bootstrap proposalsControlled

In early discovery, LLMs can propose new intent tags and mappings. Outputs must have expiry + promotion workflow.

⛔What LLMs must NOT doHard line

LLMs must not be the sole classifier for compliance-grade signals. Anything “authoritative” must be deterministic and testable.

Slide 4 — Best-of-both adoption path

Pragmatic

Adopt Approach 2 as the backbone. Use Approach 1 as a controlled accelerator only where uncertainty is high.

Ingestion and storage stay deterministic.
Semantic layer is governed core IP.
LLM outputs expire unless promoted into rules/ML.
Agents recommend; governance approves.

Slide 5 — Decision rule

Simple

If an output must be reproducible, auditable, and compliance-grade, it must be produced by deterministic rules/ML — not an LLM.