I'm really confused by "Anecdotally, most people use LLMs for ~4 basic natural language tasks" and "Most LLM applications use some combination of these four".
I'm not sure about the `ELI5` use-case, and feels like this is only true for a very limited type of use-cases people currently use LLMs for.
For conversational FAQ-type use-cases like the ones described by OP perhaps a few basic prompts suffice (although anything requiring the agent to have "agency" in its replies would necessarily require prompt engineering) - but what about all the other ways that people can use LLMs to analyze, transform and generate data?
You say this as if the goal of building a business (and measure of success) was to raise money?
From what I can see, Front has 360+ employees and generates $64M in revenue, which works out to $177K revenue per headcount. Missive generates $2M with a headcount of 4, which comes out to $500k revenue per employee.
Imo, generating almost 3x more revenue per headcount, and not having to deal with the headaches of managing a 360+ employee team (while maintaining complete ownership of your company) is the type of success that more founders should aspire to.
Thanks for the quick answer! Follow-up around this as it's a space I'm actively working in: did you use or build any tools for the labeling process, or was it Excel? :D Also, do you ultimately see/position your solution as an AI-powered exploration tool that allows humans to derive better insights, faster (but where the NLP side of things is simply to assist in this discovery process), or do you see the models (and resulting flags) eventually being able to completely replace the human intuition?
Congrats on the launch guys! Fellow Canadian from Montreal here :) I'm curious, what tools / methodology did you follow to generate the high-quality labels, and how many different labels did you end generating? I'm also very curious whether you view the discovery and generation of new labels (and accompanying high-quality training datasets) as a continuing and core part of your development going forward?
For conversational FAQ-type use-cases like the ones described by OP perhaps a few basic prompts suffice (although anything requiring the agent to have "agency" in its replies would necessarily require prompt engineering) - but what about all the other ways that people can use LLMs to analyze, transform and generate data?