Internal documentation knowledge base.

Many companies have documentation scattered between services - google drive, confluence, maybe something bespoke for engineering documentation. There’s also ad hoc documentation happening every day - jira tickets, github comments, slack convos, video meetings. It’s a skill in itself to proficiently search disparate services to answer questions. Knowledge is lost every time someone leaves a company. Knowledge also degrades over time - we forget the context of our decisions, later leaving us to wonder why we chose something particular in the past. Or even if we deliberately chose to do something or it’s just a consequence of other decisions. Imagine onboarding. It’s a huge pain point.

Now imagine as much of that context that’s recorded in some way is aggregated, ranked, and easily searchable in a single interface. Sounds pretty sweet in comparison to the reality many startups face.

This is the top use case I would have for chat gpt builder, but there are other LLMs not managed by a third party that are a much better choice if data governance is a concern. I imagine anything training on gpt builder is available to chat gpt itself. Perhaps anonymized to some level, but in practice with tech like this that really just means it’s impossible to attribute the creator after the data enters the model. So anonymization by consequence, not necessarily design.

My dayjob has guidelines about what we are allowed to feed into chat gpt prompts, what policies to follow if using GitHub copilot, etc. There is no way we would ever get something like handing over our IP to open ai approved. However, not every company has resources to train its own internal LLM so the trade off is probably worth it, not important enough to consider right now. It’s also likely these details are buried in legalese hiding within the terms so people agree to it without even realizing.