engineering · 1 min read
Drift Detection for LLM Apps
LLM apps drift even without code changes - providers update silently, user inputs evolve, KBs grow. Here is how Bhogar AI detects drift early.
BABhogar AI TeamProduct & Engineering
LLM apps drift even when nobody changes anything. Providers update models, user inputs shift, KBs grow. Drift detection turns silent decay into an actionable signal.
Why it matters
The signals are subtle: small changes in latency, token counts, refusal rates, and answer-similarity over time. Production drift detection watches all of them with statistical baselines.
How Bhogar AI approaches it
Bhogar AI tracks per-prompt, per-model, per-KB baselines and alerts on statistically-significant deltas across cost, latency, refusal and answer-similarity dimensions.
- Per-prompt and per-model baselines
- Statistical drift detection across multiple metrics
- Anomaly alerts to your incident channel
- Drilldowns by cohort and tenant
- Drift correlation with provider model versions
What you get
Teams catch silent model regressions and KB-quality decay weeks earlier than they would by waiting for user complaints.