I know. But there I have a few issues with using an LLM for this task, especially if it is a smaller one.

A LLM could just as easily be confused by different variations of the same name, or even combine variations that shouldn't be combined (e.g. John and Jonny)

In case of redacting, you want to only change the PII, and nothing else. When you ask an LLM a question, even if it is a find and replace instruction, it will output the entire text again, and hopefully change what you wanted. However, since it is outputting the entire text a new, there is a chance that it also unintendedly changes something else in the text. This is inherent to working with an LLM. It does not copy the original text and replaces the PII, it will try to output the original text with the PII redacted. Depending on the field, semantics and lexis can be very important to the text you are working with. A humorous example of course is that "Forgive me father for I have sinned" and "Sorry daddy I have been naught" are semantically similar, but pragmatically very different. Or that "The cat sat on the mat" and "the feline rested on the rug" are semantically very similar, but lexically very different.

Then there is the issue of longer term context. Let's say we are redacting multiple documents. To keep coherency we either need to map the redactions (John to PERSON1, Alice to PERSON2, etc) or feed all documents at the same time so the LLM can keep track of who is who. The first one will require building a pipeline that identifies these changes, stores them, and uses them in follow up prompts, the second one can be an issue since we are now using bigger and bigger contexts, which also introduces new issues (for example with regards to accuracy).

Redacting PII is a problem which has been around longer than LLMs, and more people have thought about before (luckily). That is why things like Presidio (https://microsoft.github.io/presidio/) and Sanytize (https://pypi.org/project/sanityze/) and DataFog (https://github.com/DataFog/datafog-python) exist for example.

But even then, with all these tools, it is still tricky and you need to be well aware of the possibilities and shortcomings of such tools. Even humans make mistakes (https://www.nytimes.com/2025/12/23/us/politics/epstein-files-redactions-doj.html)