[dingnuts]

The tools mechanical and civil engineers use are predictable. You're confusing the things these engineers design, which have tolerances and things like that, with the tools themselves.

If an engineer built an internal combustion engine that misfired 60% of the time, it simply wouldn't work.

If an engineer measured things with a ruler that only measured correctly 40% of the time, that would be the apt analogy.

The tool isn't what makes engineering a practice, it's the rigor and the ability to measure and then use the measurements to predict outcomes to make things useful.

Can you predict the outcome from an LLM with an "engineered" prompt?

No, and you aren't qualified to even comment on it since your only claim to fame is a fucking web app

[graemefawcett]

If you're claiming those to be the success ratios you're having with AI assisted engineering, perhaps the phrase context in, tokens out might help. The relationship is symmetrical I have found.

In general, the more constraints you apply on the solution space via context, the more likely the correct solution is to stabilize.

It also helps to engineer the solution in such a way that the correct solution is also the easiest and this the most likely.

It takes time, but like most skills can be learned.

[simonw]

> No, and you aren't qualified to even comment on it since your only claim to fame is a fucking web app

Whoa, where did that come from?

[skylurk]

I know, right? I did not predict that output either.

[quequon]

Then you just engineering'd.

[skylurk]

Civil engineers deal with contractors who would misfire 100% of the time if they could get away with it.