I was at a ๐ฐ๐ฎ๐ณ๐ฒ ๐ถ๐ป ๐๐๐ฑ๐ฒ๐ฟ๐ฎ๐ฏ๐ฎ๐ฑ debating remote-work productivity with friends - we asked the same prompt to ๐๐ฉ๐ข๐ต๐๐๐, ๐๐ฆ๐ฎ๐ช๐ฏ๐ช, ๐๐ฐ๐ฑ๐ช๐ญ๐ฐ๐ต, ๐๐ฆ๐ณ๐ฑ๐ญ๐ฆ๐น๐ช๐ต๐บ... and got five completely different โ๐ง๐ข๐ค๐ต๐ด.โ Same question. Wildly different answers. And we just accepted whichever came first. ๐ณ
Thatโs when it hit me: ๐๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐๐, ๐๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต๐ฒ๐ฑ โ ๐ป๐ผ๐, ๐๐ฒ ๐ฑ๐ฒ๐ฐ๐ถ๐ฑ๐ฒ. Weโve skipped the comparison, the thinkingโฆ and plunged straight into confident - but not always accurate - answers.
Wondering why AI often misses the mark? Itโs not the model, itโs how we're prompting.
I wrote a ๐ฒโ๐บ๐ถ๐ป๐๐๐ฒ ๐ด๐๐ถ๐ฑ๐ฒ with the ๐๐ถ๐ป๐ด๐น๐ฒ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ I now use to consistently get the response I actually want (ChatGPT, Claude, Gemini).
๐๐ป๐๐ถ๐ฑ๐ฒ:
โข The mindset shift: searcher โ decider
โข The exact prompt structure (no fluff)
โข 3 copy/paste examples for work
๐ฅ๐ฒ๐ฎ๐ฑ ๐๐ต๐ฒ ๐ณ๐๐น๐น ๐ฎ๐ป๐ฎ๐น๐๐๐ถ๐ ๐ฝ๐ผ๐๐ ๐ต๐ฒ๐ฟ๐ฒ:
๐๐: ๐๐ข๐ท๐ฆ ๐ต๐ฉ๐ช๐ด ๐ง๐ฐ๐ณ ๐บ๐ฐ๐ถ๐ณ ๐ฏ๐ฆ๐น๐ต ๐๐ ๐ต๐ข๐ด๐ฌ ๐ข๐ฏ๐ฅ ๐ด๐ฉ๐ข๐ณ๐ฆ ๐ธ๐ช๐ต๐ฉ ๐ข ๐ต๐ฆ๐ข๐ฎ๐ฎ๐ข๐ต๐ฆ ๐ด๐ต๐ถ๐ค๐ฌ ๐ช๐ฏ 10โ๐ฃ๐ญ๐ถ๐ฆโ๐ญ๐ช๐ฏ๐ฌ๐ด ๐ฎ๐ฐ๐ฅ๐ฆ.
#AI #PromptEngineering #GenerativeAI #TechThinking #HyderabadTech #AIDecisions #Prompt
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