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The same handful of support tickets, copied into my training set a dozen times over, and I never noticed.
What you'll have at the end
A deduplicated training file, with an exact count of how many rows you removed
You need
A fine-tuning training file already built in your trainer's row shape, each row still traceable back to its source ticket's ID and creation date through the export or index you kept, plus a working call that turns one piece of text into one embedding vector.
Not covered
Whether the row shape itself already matches your trainer's format, and scrubbing personal information out of ticket text; this recipe assumes both are already handled and only removes rows that repeat or conflict with another row, on a file small enough to compare directly.
Leans on
Format your first fine-tuning dataset
go there first if your raw pairs aren't in your trainer's row shape yet; this recipe assumes the file already loads clean and only trims rows that repeat or conflict
Untangle five different phrasings of the same question in your dataset
go there instead if you're deduplicating a plain list of questions with no attached answer, where the wording alone decides a duplicate rather than whether two answers agree
Catch an overfit fine-tune before you ship it
go there once your file is deduplicated and trained, to check whether the model still ended up memorizing a handful of rows
Checked 15 Aug 2026
Part of the LLM Fine-tuning cookbook