Practical prompting, backed by research

Finding: an LLM anonymizing text removes exactly what it was told to — not a word more. Institution-Specific Prompting lets you catch an organization's local slang — hospital abbreviations, building names, internal codes — that falls outside the standard 18 categories of personal data and therefore stays in the text untouched. Ask the model to look for specific local categories and give "don't remove this" examples — it applies the rule across the whole document instead of guessing. Result: about 4 out of 5 previously missed identifiers are found in a single pass.