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I would guess that by fine tuning an LLM on product manuals, installation guides, FAQs and vetted and customer support cases, one could create a competent suppo
by raffraffraff 2y ago
I would guess that by fine tuning an LLM on product manuals, installation guides, FAQs and vetted and customer support cases, one could create a competent support chat bot. Using RAG you could provide it with the output of your real-time status page for the product, and use a prompt that had the ability to forward issues to a human if the customer seemed unhappy.
I'm not sure if there's a concrete example of this in reality, but why wouldn't it work, greed and incompetence aside?
- portaouflop 2y agoAt work we use a chatbot trained on our docs, it’s pretty good and sees lots of usage. Of course people can also just read the docs, but many prefer the bot.
- aenis 2y agoSame here, but adoptoion is a challenge. People tend to stop using it after the first instance of a bullshit answer - they lose trust completely. Which is, seemingly, inevitable.
- falcor84 2y agoWhy is it inevitable? I think that the proper way to model it is as a multi-armed bandit - a bad response from an agent should reduce your likelihood of using it again but not to zero. If users have sufficiently good alternatives, I expect usage of the AI agent would drop down, but it seems to me that the other options are generally worse (and often similarly likely to give bullshit answers), such that users will over a long timeframe would settle on a relatively high likelihood of using the AI.
- threeseed 2y agoGithub Support is an example: https://support.github.com https://support.github.com With information about how it was implemented: https://docs.github.com/en/support/learning-about-github-support/about-copilot-in-github-support https://docs.github.com/en/support/learning-about-github-sup...