[CuriouslyC]

Prompt engineering is honestly not long for this world. It's not hard to build an agent that can iteratively optimize a prompt given an objective function, and it's not hard to make that agent general purpose. DSPy already does some prompt optimization via multi-shot learning/chain of thought, I'm quite certain we'll see an optimizer that can actually rewrite the base prompt as well.

[maciejgryka]

I hear you and am planning to try DSPy because it seems attractive, but I'm also hearing people with a lot of experience being cautions about this https://x.com/HamelHusain/status/1777131374803402769 so I wouldn't make this a high-conviction bet.

[CuriouslyC]

I don't have the context to fully address that tweet, but in my experience there is a repeatable process to prompt design and optimization that could be outlined and followed by a LLM with iterative capabilities using an objective function.

The real proof though is that most "prompt engineers" already use chatgpt/claude to take their outline prompt and reword it for succinctness and relevance to LLMs, have it suggest revisions and so forth. Not only is the process amenable to automation, but people are already doing hybrid processes leveraging the AI anyhow.

[namaria]

It strikes me as bad reasoning to look at a system that is designed to be very complex and stochastic as a way to get some creativity out of it ("generative AI" so to speak) and try to bolt down added apparatus to get deterministic behavior out of it.

We have deterministic programming systems. They're called compilers.

[CuriouslyC]

I think you're missing the point. If an application had simple logic, the program would have been written in a simple language in the first place. This is about taking fuzzy processes that would be incredibly difficult to program, and making them consistent and precise.