Hey everyone, I'm curious if anyone else has noticed how prompt engineering changes the quality of outputs with models like ChatGPT? Do you also find that even simple prompts like "ask open-ended questions" or "think step by step" can make responses 50-100% more useful? Especially in technical topics, how much does adding detailed context really impact the results? Let's experiment together—what prompt techniques are you all trying out? 🤔
Does prompt engineering really boost ChatGPT's efficiency by 100%?
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Well, I think prompt engineering does significantly boost efficiency, even if it's not always a 100% improvement. In my experience, when I switched from a simple "open-ended" prompt to something like "Think step by step, then answer and explain each step," the responses to technical questions became both more detailed and more accurate. For example, when debugging a Python issue, instead of just saying "help," if I asked ChatGPT to "Understand the problem, list possible causes, and then provide the corrected code snippet," the output was way more functional.
The impact of detailed context is huge. If I ask for "a good example of database design" instead of "Create a normalized database schema for an e-commerce site using PostgreSQL, including customer, product, and order tables," the quality of the response changes drastically. So, it really pays off to tailor your prompts as much as possible.