If the requirement is “always the same answer for the same inputs,” an LLM is usually the wrong core. If the requirement is “draft under constraints, then verify,” it can be excellent.
Prefer rules when the law is the law
Eligibility, pricing tiers, and hard thresholds should be encoded where they can be tested, versioned, and audited. Use models to explain, summarize, or prepare—not to silently redefine policy.
Compose: rules first, models second
The durable pattern is structured state + explicit transitions, with AI assisting at the edges: extraction, classification suggestions, and templated drafts with citations back to source fields.
Testing and operations still want determinism
Non-determinism makes traditional QA nervous for good reason. In production, we isolate model calls behind interfaces with explicit schemas, golden fixtures for regressions, and evaluation sets that track drift over time—not a one-off leaderboard screenshot.
That does not mean models are banned from core paths. It means the core path has guardrails: thresholds, secondary checks, and human-readable reasons when automation declines. The goal is predictable failure modes: fail closed, route to review, and preserve evidence.
This pattern is central to AI agents where automation earns trust, especially for teams in regulated workflow case studies.
For deeper context, compare this with controlling context costs in production agent pipelines and model rollout and rollback change management.
Related case study: defense compliance workflow case study.