In this module you will
Name the common ways models fail and tie each to its cause from modules 1 to 6.
Write a probe that tests one failure mode on purpose, and check the probe itself.
Use cheap detectors (grounding, invented numbers, self-consistency) and know their blind spots.
Design an agent step so that a failure is caught early instead of passed on.
A single wrong answer in a chat is an annoyance. In an agent the answer becomes the input to the next step : a wrong fact feeds a tool call, the tool result feeds another call, and the error compounds. If each step is right 95% of the time, a 10-step task is right only 60% of the time (0.95 to the tenth power).
Knowing how models fail is how you build the checks that keep that number high.
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Failure Usual cause First thing to try Hallucination predicts likely text, not true text ground in sources; cite; check numbers Inconsistency sampling; wording changes fixed settings; test several phrasings; vote Lost in the middle attention is uneven over long input put key facts at the ends; send less; retrieve Sycophancy preference training rewards agreement ask neutral questions; do not reveal your guess Stale knowledge training stopped at a cutoff date give it current data; use search tools Instruction drift long chats dilute early rules restate rules; keep them in the system prompt Six failure modes, where they come from and the first thing to try.