[wise0wl]

I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM. A continuing architectural diagram is constructed and graph is updated until all cases are covered and then the LLM writes the code, writes the validations, pushes to CI environments, runs tests, schedules prod deploy (by looking at company event schedule), gets CAB approval, deploys code, tests in prod, and fixes regressions.

I'm not saying this is the correct thing, but companies are implementing it and it is "working". I don't think keeping our head in the sand is helping.

[RaftPeople]

> I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM.

But the LLM is not aware of how the business works and why, so someone needs to work with the business to extract the information. Typically it's not well documented.

[bizzletk]

> someone needs to work with the business to extract the information. Typically it's not well documented.

LLM extraction of the information from the Product Owner is becoming the way to overcome poorly-documented business context.

Non-technical folk are using things like `/grill-me` [0] to seed the LLM with the long-tail complexities that they didn't know they didn't know they needed to put out.

[0] https://www.aihero.dev/my-grill-me-skill-has-gone-viral

[IanCal]

They can ask, they can do a back and forth and they can write documentation to be used from that point onwards and write it in a common style and structure.

These are language models, being able to talk through something with them and have them extract some information is what they excel at. Given that you’d probably get a halfway decent result with a literal fixed set of questions (an Eliza level docbot) gpt 5.5 is going to nail that as a task.

[ijustlovemath]

is it working though? The main outcome we've seen with companies that drink the AI Kool aid en masse is buggy unstable systems. clearly there's a level of rigor that's being missed for ship velocity