> because my prompts are in natural languages, and hence ambiguous.
Legalese developed specifically because natural language was too ambiguous. A similar level of specificity for prompting works wonders
One of the issues with specifying directions to the computer with code is that you are very narrowly describing how something can be done. But sometimes I don't always know the best 'how', I just know what I know. With natural language prompting the AI can tap into its training knowledge and come up with better ways of doing things. It still needs lots of steering (usually) but a lot of times you can end up with a superior result.
Yes. LLMs are search engines into the (latent) space or source code. Stuff you put into the context window is the "query". I've had some good results by minimizing the conversational aspect, and thinking in terms of shaping the context: asking the LLM to analyze relevant files, nor because I want the analysis, but because I want a good reading in the context. LLMs will work hard to stay in that "landscape", even with vague prompts. Often better than with weirdly specific or conflicting instructions.
But search engines are not a good interface when you already know what you want and need to specify it exactly.
See for example the new Windows start menu compared to the old-school run dialog – if I directly run "notepad", then I get always Notepad; but if I search for "notepad" then, after quite a bit of chugging and loading and layout shifting, I might get Notepad or I might get something from Bing or something entirely different at different times.
Indeed, which is not all that different from LLM code generation, to be honest.
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