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.
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?