Common failure modes
What are the common ways prompting goes wrong?
Prompting usually fails in a few recognizable ways: the model invents facts, drifts off your instructions, gets buried in too much context, or guesses at something you left vague. Each has a known fix.
When a prompt does not give you what you wanted, it is rarely a mystery. The trouble almost always falls into one of a few patterns. Knowing the pattern is most of the fix, because each one has a known cause and a direct response.
The four covered in this topic are: the model stating something false with confidence (hallucination), the model wandering away from what you told it to do (instruction drift), the model getting lost in an input that is too long, and the model guessing because your request left room to guess (ambiguity).
None of these mean the model is broken. They are normal behaviors of a system that predicts likely text rather than looks up verified facts. Once you see a failure as one of these patterns, you can reach for the matching fix instead of rewriting the whole prompt and hoping.
The pages that follow take each pattern in turn: why it happens, how to spot it, and what to change.