Continuous Evaluation and Safe Rollout for LLMs

Large Language Models (LLMs) are probabilistic and nondeterministic, making continuous evaluation and controlled rollouts essential. You cannot set and forget. Production teams maintain golden datasets segmented by scenario: short question answer, multi turn conversation, safety edge cases, domain specific tasks. These datasets run nightly in Continuous Integration (CI) pipelines and after every model, prompt template, or provider change. Evaluation combines automated LLM as a judge scoring with periodic human review on sampled outputs, calibrating judges per domain to reduce bias. Netflix and Meta use this pattern to catch regressions before they reach users. Shadow deployments and canary rollouts provide the safety net. Shadow mode routes 1 to 10 percent of live traffic to the new model without serving results to users, logging outputs for offline comparison. This detects silent failures like refusal rate spikes, verbosity changes, or hallucination increases that only surface with real traffic diversity. Canary rollouts gradually shift 1 percent, 5 percent, 10 percent of traffic to the new model, with automatic rollback triggered when p95 latency exceeds threshold (for example, 2 seconds), refusal rate increases more than 2 percentage points, groundedness score drops below 0.75, or cost per request rises more than 30 percent. For high variance LLMs, longer shadows on representative traffic are necessary because rare edge cases and long tail prompts only appear at scale. Vendor model updates introduce unique risks. When a provider ships version x plus 1, teams observe semantic style shifts, changed refusal rates, tokenization differences causing context overflow, or cost changes from longer default outputs. These changes break downstream user experience or blow cost budgets if not caught. The mitigation is pinning model versions explicitly in production, recording decoding parameters (temperature, top k, top p) with each trace, and running A/B tests with matched prompts before promoting. Gate promotion on equal or better latency, cost, refusal rate, and groundedness at p95, not just average metrics. Failure modes include observability blind spots and feedback loop risks. Over aggressive log sampling (for example, 1 percent sampling) removes the exact tail events needed to debug rare hallucinations or prompt injection attempts. High cardinality labels explode metrics storage, forcing teams to drop dimensions and hide cohort specific regressions (for example, non English prompts degrading faster). Security blind spots emerge when input output classification is missing; toxic or Personally Identifiable Information (PII) outputs slip through. Abuse spikes from Distributed Denial of Service (DDoS) or scraping manifest as sudden Application Programming Interface (API) latency anomalies, requiring rate limiting and abuse classification integrated into the observability stack.

Meta content moderation: vendor LLM update changed refusal rate from 2 percent to 8 percent, caught by shadow deployment with A/B comparison before full rollout prevented user facing failures

Netflix recommendation descriptions: shadow mode on 5 percent traffic detected 15 percent hallucination rate increase from stale retrieval index, triggered rollback and index refresh before canary

Uber customer support: pinned model version with temperature equals 0.7 and top p equals 0.9 in production config, new provider default temperature equals 1.0 caused verbose responses costing 40 percent more tokens until config override deployed

Airbnb search assistant: canary rollout to 10 percent triggered automatic rollback when p95 latency spiked from 1.8 seconds to 3.2 seconds due to longer context in new prompt template, reverted within 5 minutes