Any form of engineering involves a certain degree of reliance on abstractions, e.g. a civil engineer really doesn't need to know everything about the molecular

Any form of engineering involves a certain degree of reliance on abstractions, e.g. a civil engineer really doesn't need to know everything about the molecular structure of steel and concrete to successfully design and build a structure such as a bridge. What does matter is having stable, predictable abstractions that you can rely on, so in software engineering it would be a mistake to rely on system-dependent undefined behavior when using a language like C++. You also don't necessarily need to know what kind of low-level optimizations the compiler and linker are using to generate your executable binary, though in some cases it could be important. With LLMs, however, it's not really clear if the same prompt always gives the same kind of output, and you can do 'regenerate response' with something like ChatGPT to see this in action. In some sense, 'prompt engineering' can be more like 'interviewing an engineer' in that LLMs seem to provide better outputs if you start with a broad, general question and then narrow down to your specific interest over a series of questions. Helping the LLM out by defining context, asking it to expand on a specific output it generated, pointing out where it may be hallucinating etc. all appears to improve the quality of output but I don't know if this kind of iterative process is really 'prompt engineering', maybe 'prompt optimization' is a better word for it? In other words, writing a series of prompts to an LLM that gets you the answer you need feels quite unlike writing a Python script to automate some task. You can plan the latter out from start to end, but the former is this back-and-forth process.