AI Integration & Production MLOps

An AI service can return an empty or nearly empty answer while requests appear successful. The issue may follow a response parser change, token limit, safety filter, or tool output formatting shift. What to check Track response body length, completion status, model and prompt version, tool calls, safety outcomes, and downstream parsing. Compare...

An assistant may route new customer requests to the wrong category after model, prompt, or product changes. Aggregate resolution rates can hide errors concentrated in a specific intent. What to check Review misrouted examples, confidence and fallback thresholds, recent taxonomy changes, language and channel segments, and downstream queue outcomes. Use...

An assistant may retrieve two valid but conflicting documents and choose one without showing the disagreement. Users need to know when policy or guidance sources conflict. What to check Track multiple-source retrieval, document authority and dates, citation selection, and examples where answers disagree. Test questions that span draft and approved...

An assistant may answer correctly only because of context from a prior message, session, or cached retrieval. New users or isolated requests can receive incomplete or contradictory answers. What to check Test questions as standalone requests and in multi-turn conversations, inspect session and cache behavior, and compare retrieved sources. Ensure...

Small wording changes can cause an assistant to retrieve different sources, change confidence, or give contradictory answers. Users may receive materially different guidance for the same intent. What to check Create paraphrase sets for important questions and compare answer content, sources, refusal behavior, and action suggestions across model...

A model update can change responses even when prompts and users appear unchanged. Data, system instructions, retrieval indexes, parameters, and upstream services can also shift behavior. What to check Capture model and prompt versions, retrieval sources, settings, representative inputs, timestamps, and evaluation results. Compare controlled examples...

Model accuracy alone may not show whether an AI feature helps users. A feature can return plausible answers while increasing correction work or delaying task completion. What to check Define the user task and baseline, select outcome measures such as completion, correction, escalation, and time, and segment results carefully without collecting...

A model may handle routine inputs well but remain uncertain or inconsistent on edge cases. Human review is important when errors have material consequences or the model lacks adequate evidence. What to check Assess impact of a wrong output, confidence calibration, data coverage, reversibility, and user ability to challenge a result. Test difficult and...