AI feature broken in production.Fixed this session.

PM assigned in 10 minutes. AI engineer starts diagnosing within the hour using your error monitoring (Sentry, Datadog) and LLM provider logs. AI feature errors in production fall into a small number of categories: 429 rate limit errors from hitting your API tier quota during usage spikes (fix: implement exponential backoff with jitter and request queuing), 500 errors from the LLM provider during a partial service disruption (fix: add a fallback model or graceful degradation), timeout errors from LLM responses taking longer than your configured timeout (fix: adjust timeout configuration and implement streaming responses), or context length errors from requests exceeding the model context window as your users send longer inputs (fix: implement input truncation or chunk-and-summarise strategy).

Development-to-production AI failures are almost always environment-specific. The most common causes: the production API key is a different tier from your development key with different rate limits and model access, the production environment variable for the API key or model name is not set or is set to a different value, the production database or file storage does not have the same data as development so RAG retrieval returns empty results, the production deployment has a different version of the LLM SDK than development causing breaking API changes, or the production network configuration blocks outbound HTTPS requests to the LLM provider on the specific port used. The engineer runs a systematic environment comparison in the first 15 minutes to isolate the divergence.

Yes. PM scopes the full cycle into a Full Day session: diagnosis of the accuracy or reliability failure (15 minutes), root cause identification (30 minutes), prompt engineering or infrastructure improvement (2-3 hours), evaluation against a test set of your real production queries using RAGAS or a custom eval suite, and deployment. The session ends when before-and-after metrics prove the improvement and the fix is live in production. For accuracy improvements specifically, the engineer also adds an ongoing evaluation job that runs nightly on a sample of production queries and alerts if quality drops below your threshold preventing silent degradation from future model or data changes.

PM implements evaluation metrics as part of the fix session you should never trust a fix is working without measurement. For LLM accuracy: the engineer builds a test set of 20-50 representative queries with expected answers, then runs RAGAS evaluation metrics (faithfulness, answer relevancy, context precision) against both the current broken version and the fixed version. For reliability: the engineer measures error rate and P99 latency before and after the fix using your monitoring stack or a simple load test. For user-facing quality: if you have existing user feedback data (thumbs up/down ratings, escalations, corrections), the engineer uses that as the ground truth for measuring improvement. Before-and-after comparison is delivered in a written report at session end.

Five most common production AI feature failures: first, context window overflow user inputs grow longer than the model context limit, causing truncation that cuts off critical instructions or information (fix: implement smart truncation preserving the most relevant context); second, silent hallucination the model gives confident wrong answers with no error signal, users trust incorrect output (fix: implement output validation and confidence scoring); third, RAG retrieval returning irrelevant results the vector database returns chunks with surface-level similarity that are semantically wrong (fix: tune similarity threshold, add re-ranking, improve chunking strategy); fourth, prompt injection from user input overriding system instructions (fix: input classification and output validation); fifth, cost overrun from unexpectedly long conversations consuming more tokens than estimated (fix: implement conversation summarisation and token budget management).

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