What is the best way to approach data quality for Large Language Models?

A useful way to look at What is the best way to approach data quality for Large Language Models? is to focus first on data and quality. That tends to make the trade-offs easier to compare. It also gives you a cleaner baseline if you want to compare tools or settings later.

For What is the best way to approach data quality for Large Language Models?, I would separate the problem into setup, output quality and repeatability. data and quality fits into that because it can change the result quite a lot depending on the workflow. The original point — "I have been exploring Large Language Models and wanted to ask people who have tried it in practice. I am particularly interested in data quality and security. I would like to comp…" — gives a useful starting point for that comparison.