For most dev work I ask claude code / codex to plan larger work as epics and stories (after ideation / brainstorm if needed). I find that this works well to let the AI, my coworkers and me track what has been done so far and what's left in the backlog. Sometimes I direct it on how to split the work, sometimes I don't.

When analyzing my session logs, I tried to look at how story size relates to how much work the AI had to do to fully implement it (made easier by my sessions always having epic#.story# - like 2.6 - in their name). Neither "story size" nor "AI work" have fully obvious metrics, so I looked at a variety of things, like length of story plan, number of lines of codes written for the story, number of different files read/written (and for AI work: number of tool calls, number of tokens for main thread / implementer / reviewer subagents, etc.).

The pattern is always pretty much the same: smaller stories are less token-efficient. One major factor for this is that in bigger stories, the average number of lines of code written per tool call is significantly higher (and not just a little bit, if I remember correctly it's a factor of 1 to 3 between my smallest and largest stories).

None of this is scientifically rigorous, but it's consistent over a fairly significant sample (about 60 stories up to this point). I'm also only measuring amount of work and not quality, but when I find things wrong with a story, I generally ask the AI to fix the issues in the same session, so the two should be correlated (sometimes the issues are large enough that they require new, separate stories to fix, but this is normally caused by problems in ideation and/or planning, not implementation). This is enough to encourages me to ask for more rather than less per story. It might also not apply to everyone, I'm sure this is dependent on everyone's workflow and way to work with the AI. But yeah, that's the sort of stuff you can explore when analyzing your session logs.