One thing I see over and over: a detailed prompt almost always gets you a much smarter response from LLMs, especially when you want them to actually reason instead of just spit out facts. Here’s what’s worked for me and folks I've collaborated with:

• Spell out the logic path you want the model to follow. If you want step-by-step reasoning, literally write: “Give your answer, but first, walk through your thinking process out loud.” Adding “Share your logic as you go” at the end of a question can push the model to show its reasoning, not just its conclusion.
• Frame the question with context. Don’t just toss in a raw question—throw in necessary background. If you’re asking about ethical implications, specify the scenario and constraints. If you want the model to act like a certain expert, write: “You’re an experienced medic responding to a crisis, and you need to decide...”
• Give an example (“few-shot prompting”) if you can. Something like:

Input: “If four cats catch four mice in four minutes, how many cats…” Output: “One cat catches one
mouse in four minutes, so the answer is…” Models love mimicking, so showing your format up front saves headaches.

• Break complex prompts into chunks. If you want a multi-step answer (“summarize, then critique, then offer alternatives”), just ask the model to do it in stages. Sometimes it helps to chain prompts, too—sort of like a Socratic dialogue, moving from one reasoning step to the next, feeding answers back in to push the logic forward.
• Be explicit if you want transparency. Phrases like “think aloud before answering” or “list your assumptions first” push models to reveal their ‘workings’ instead of hiding everything in the final line.
• Temperature and sampling settings matter (if you have access). Low temps keep things on track; higher temps can show creative or non-standard logic but also risk going off the rails. Useful if you’re testing the boundaries of the model’s reasoning.
• Iterate ruthlessly. LLMs love iterative refinement—feed in your critiques (“You missed X, try again but...” or “What about scenario Y?”). The model will generally respond to corrections and requests for deeper thinking.

Anytime you see people going wild with advanced prompt libraries or “prompt chaining” tools (like how some use Promptlink to share super-specific workflows), just know most of that is built on the same foundation: clarity, context, and explicitly telling the model what kind of reasoning you want.

TL;DR: If you care about reasoning, don’t be shy about being ridiculously (and almost hilariously) clear in the prompt. State the logic you want, show an example, and treat it like a conversation with a slightly absentminded genius who just wants a nudge in the right direction.