You give it a problem, you then refine that problem where a fast, cheaper model asks you questions which you answer to get a better input prompt. You then choose a MA strategy for example take problem break up to sections then final judge concludes or you do multi turn where agents debate then judge summarises debate.

The best approach is what I call 'all angles' where all these strategies run in parallel the final meta-judge synthesise the response - the most useful part of this which I recently added is a view to see the variance in each strategy.

Been using this for life stuff - housing search, schools, family challenges!

Perhaps I should make a video of it in action if people in HN community interested let me know.

Such drastic changes tell me that pricing of tokens is arbitrary, and AI business is running out of money fast.

I'm seeing a ratio of around 10:1 in my usage. A vast majority of the tokens consumed are on the input side. The agent will often read a million tokens just to patch one line of code.

I think if you are seeing something closer to 1:1 or more on the output side, there is either a problem with the agent or the codebase is new / empty.

[1] https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Stee...

Was in a meeting reviewing a potential new product, it was going well until they showed us that they had added AI to it (of course they have). It was pretty obviously just shoehorned in, and one part of that obviousness was that they had a column that showed how many tokens it took to make each query.

I asked who is paying for the tokens, they said its included in the license. I said, so is there a budget or is it all you can eat. they said good question they didnt know and would get back to me. I said the reason i asked was just one query there had a 250k token burn on it. and it was a fairly simple query about one device.

then, one of the execs on their side was heard saying out loud "Why are we even showing this to the customers?"

it have us quite a chuckle. But lesson learned... the cost of adding AI to anything isnt really being accounted for let alone the true cost of actually running the AI.

all things AI are going to get more expensive. even if you dont want the AI aspect.

Maybe soon companies will look at how engineers can optimize the token efficiency of AI.