"Prompt engineering is the process of crafting inputs, or prompts, to a generative AI system that lead to the system producing better outputs. That sounds simple on the surface, but because LLMs and other gen AI tools are complex, nondeterministic " black box " systems, it's a devilishly tricky process that involves trial and error and a certain degree of guesswork - and that's before you even consider that the question of what constitutes "better output" is itself difficult to answer."
"Most people's experience with gen AI tools involves directly interacting with ChatGPT or Claude or the like. For those folks, prompt engineering techniques represent a way to get better answers out of those tools. Those tools are becoming increasingly built into business software and processes, so that's a strong motivation to improve your prompts, just as the first generation of web users learned quirks and tricks of Google and other search engines."
Prompt engineering means designing prompts to coax better outputs from generative AI systems. Large language models and other generative tools behave as complex, nondeterministic "black box" systems, making prompt design a trial-and-error process that often involves guesswork. A range of techniques, some intuitive and some less so, can improve results and are becoming important skills. End users interacting with chatbots benefit from better prompts, and developers must build orchestration layers and system prompts in enterprise applications to reduce user burden. Widespread embedding of generative AI in business software increases the incentive to master prompt techniques.
Read at InfoWorld
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