Why are feature flags and config-driven behaviour important in LLM applications?

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LLM behaviour changes often (prompts, models, thresholds, retrieval settings), and many changes are risky in ways you only discover in production. Feature flags and remote configuration let you roll out changes gradually (by percentage, tenant or segment), run A/B tests, switch models or disable features instantly during incidents (kill switches), and decouple configuration changes from code deploys, all with audit trails.

Uses

Use	Example
Gradual rollout	New prompt v15 to 5% of users, then 25%, 100%
A/B testing	Model A vs Model B for tenant segments
Kill switch	Disable auto-send replies if a quality alert fires
Fallback routing	Switch the primary provider during an outage
Per-tenant config	Enterprise customer requires in-region model; another wants a stricter guardrail
Cost controls	Lower max output tokens for the free tier

Good practices

Flags and configs are versioned and audited (who changed what, when).
Validation on config changes (schema checks; a prompt change still needs evals, because flags aren't a way around review).
Clean-up: remove stale flags to avoid complexity.
Consistent assignment (sticky per user) for experiments.

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Why are feature flags and config-driven behaviour important in LLM applications?

Uses

Good practices

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