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yeah so in this recording I will talk about uh the anonymity like how you can identify tii data in your um in in the documents that you feed to llm and ensure that the pii data is removed before the language model can respond to your question based on the context you uh provide to it following the rack pattern so ideally how I ensure that I do not share any pii with my llm so before um I show you the the technique that or one of the techniques that can be used not only technique the technique as well as a python package from Microsoft we can use that to uh anonymize the data but before I show that I wanted to spend some time on this slide to explain where do we need anonymization of the content now Iamp;#39;m not going to talk about the rag pattern in this recording rag is retrieval augmented generation Iamp;#39;m assuming uh the people who will look at this recording they are aware of the rack pattern now in the rack pattern we have two steps the first step is where I take all the d
