text: •**Quality:**The qualities of the training data impact the outputs a tool can produce. 
  Consider an LLM trained on gibberish; it can learn the patterns of that gibberish and, 
  in turn, produce related gibberish, but will not be very useful for writing an argument 
  or a story, for instance. In contrast, training an LLM on books with well-constructed 
  arguments or crafted stories could serve those purposes. While “well-constructed” 
  and “crafted” are necessarily subjective, the traditional role of editors and the 
  publishing process can provide a useful indicator for the quality of writing inside of 
  books. What’s more, metadata for books — information such as the title, author and 
  year of publication — is often more comprehensive than metadata for information 