Almost every production LLM bug is diagnosed from inputs, outputs and logs. Interpretability earns its keep in a narrow set of cases, and knowing which ones is worth more than knowing the techniques. This page is that list, with the cases where it is the wrong tool named first.
The honest starting position
If your model is producing bad output, the ordered list of things to do begins: read the actual prompt that was sent, including everything your framework appended; check the retrieved context for the failing case; check whether the failure reproduces at temperature 0; and check whether it started at a specific deploy.
Those four steps resolve the overwhelming majority of real incidents. The internals of the model are the last place to look, not the first, because they are the part that did not change — and the systematic version of that checklist is a better use of an afternoon than any technique on this page.
What follows is for the residue: problems that survive that pass, and where a signal from inside the model is genuinely the cheapest way forward.
