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Rahul Agarwal

Founder | Agentic AI... • 1d

Getting better results from AI often comes down to how you prompt it. If you’ve been working with LLMs, you’ve probably tried: 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 → Step-by-step reasoning, building one step at a time until you reach the answer. 𝗧𝗿𝗲𝗲 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 → Explore multiple ideas in parallel, evaluate them, and pick the best path. 𝗥𝗲𝗔𝗰𝘁 → Combine reasoning with actions, observing outcomes and adjusting along the way. 𝗗𝗶𝘃𝗶𝗱𝗲 & 𝗖𝗼𝗻𝗾𝘂𝗲𝗿 → Break tasks into subtasks with different agents, then combine results for the final output. Each has its strengths: ✅ Use 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 for math/logic. ✅ Try 𝗧𝗿𝗲𝗲 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 for creative brainstorming. ✅ Apply 𝗥𝗲𝗔𝗰𝘁 for dynamic decision-making. ✅ Go with 𝗗𝗶𝘃𝗶𝗱𝗲 & 𝗖𝗼𝗻𝗾𝘂𝗲𝗿 for complex, multi-step tasks. I’ve broken them down visually below 👇 Which 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 do you find yourself using the most? Repost for others who want to understand this.

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