After failing with three reasonable ideas, they solved the problem with an idea that previously would have seemed unlikely to work (get similarity to past known nits and use a threshold of 3 similar nits above a certain cutoff similarity score for filtering). A lot of applications of ML have a similar hacky trial and error flavor like that. Intuition is often built in retrospect and may not transfer well to other domains. I would have guessed that finetuning would also work, but agree with the author that it would be more expensive and less portable across different models.