Cite as: Real Problem AI problem “Why do my LLM evals pass in dev and fail the moment real traffic hits?”. Opportunity score 7.5 out of 10 (severity 8, AI feasibility 7, market signal 8, competition gap 7). Category AI / Agents. Trend LLMOps. Source signal: Hamel Husain and Eugene Yan applied-LLM essays 2026, Latent Space podcast guests on production gaps.. Canonical URL: https://www.realproblem.ai/archive/why-do-my-llm-evals-pass-in-dev-and-fail-the-moment-real-traffic-hits.
Why do my LLM evals pass in dev and fail the moment real traffic hits?
Golden-set evals stay green. Production CSAT silently degrades. The gap is a known industry problem with no off-the-shelf fix.
Who has it: AI platform teams running customer-facing LLM features at 1,000-100,000 calls per day.
Evidence
Teams describe a small hand-curated eval set passing while production handles a huge variety of conversations, with customer escalations coming from the gap between the two.
Our summary of a complaint that recurs in public posts, not a quote. Nobody submitted it to Real Problem AI.
Seen in: Hamel Husain and Eugene Yan applied-LLM essays 2026, Latent Space podcast guests on production gaps.Scoring breakdown
Existing players
- Braintrust · Best for static golden sets
- LangSmith · Trace-first
- Ragas · RAG-specific
What they are missing
Production-traffic-derived evals: sample real conversations, cluster by intent, auto-promote representative cases into the golden set with a weekly refresh and rationale.
Stack hint
#AI32 · Canonical URL: https://www.realproblem.ai/archive/why-do-my-llm-evals-pass-in-dev-and-fail-the-moment-real-traffic-hits