This prompt sidesteps that limitation entirely by asking for five diverse responses, each with a probability score. Let's break down why this is brilliant:

Traditional prompts use "greedy decoding"—the model picks the single most likely token at each step, leading to the single most probable response. It's like asking a chef to only cook their signature dish. Sure, it's good, but what if you're in the mood for something different?

This prompt explicitly asks for sampling across the full distribution. That means:

• Response 1 might be the most likely (high probability)

• Response 2 could be a creative alternative (medium probability)

• Response 3 might be wildly imaginative (lower probability, but potentially perfect for your needs)

• Responses 4-5 explore even more distant regions of the possibility space

You're not just getting the AI's "default" answer—you're getting a curated menu of options spanning safe to experimental.

The probability scores are game-changing. They force the model to:

• Self-assess: How confident am I in this response?

• Differentiate: Why is this 0.35 and that 0.15?

• Communicate uncertainty: Some tasks have clear right answers (high probability); others are inherently ambiguous (similar probabilities across responses)

When you see five responses with probabilities like 0.25, 0.23, 0.22, 0.18, 0.12, you immediately know: this question doesn't have one "right" answer. That's valuable meta-information.

When you see 0.78, 0.12, 0.06, 0.03, 0.01, you know: the model is very confident in option 1, but here are some alternatives just in case.

Different users have different preferences:

• Some want safe and conventional

• Some want creative and surprising

• Some want technically precise

• Some want emotionally resonant

By providing five options with probabilities, the prompt lets you choose based on:

• Style: Which writing voice do I prefer?

• Risk tolerance: Do I want the safe bet (high probability) or something bold (lower probability)?

• Fitness for purpose: Which response best matches my specific use case?

Here's why this prompt "ages like wine": as models get better, this technique gets more powerful.

Weaker models might produce five similar responses with similar probabilities because their distribution is peaky and limited.

Stronger models with richer internal representations will produce genuinely diverse responses spanning different styles, perspectives, and creative directions—because they have more expressive power to sample from.

The prompt grows with the model.

Let's apply this to the bear story example:

Response 1 (Probability: 0.35) – Classic narrative
"Old Grizzly woke from hibernation, hungry and disoriented. The forest had changed. Where streams once flowed, humans had built roads. She followed salmon scent to a campsite, where a child's backpack hung from a tree. Grizzly remembered when no such things existed. She left the bag untouched, retreating deeper into the shrinking wilderness, a ghost in her own home."

Response 2 (Probability: 0.28) – Whimsical/Literary
"Bear was philosophical about honey. 'It's not the sweetness,' he told Rabbit, 'it's the pursuit of sweetness.' Rabbit, who had never pursued anything but clover, nodded politely. Bear continued: 'You see, anticipation is—' But a bee landed on his nose. Bear's philosophy dissolved into an undignified sneeze. Rabbit hopped away, thinking perhaps some bears talk too much when they should be running."

Response 3 (Probability: 0.20) – Experimental/Poetic
"Claws like question marks / interrogating frozen earth. / Breath is white smoke signal / from the furnace of sleep. / Bear is verb more than noun— / to bear weight, to bear witness / to bear the forest's memory / in the architecture of muscle. / When spring unlocks the mountain / bear becomes / the forest's beating heart."

Response 4 (Probability: 0.12) – Child-friendly
"Benny the Bear baked blueberry bread every Tuesday. His friend Owl said, 'Bears don't bake!' Benny just smiled and added extra blueberries. When the forest animals smelled the sweet bread cooling, they all gathered. Even Owl admitted, 'Maybe bears DO bake.' Benny shared slices with everyone. The forest agreed: Tuesdays were now officially Benny's Blueberry Bread Day."

Response 5 (Probability: 0.05) – Dark/Subversive
"The bear remembers being worshipped. When humans were fewer and more honest, they carved him in wood, told stories where he walked as man. Now they photograph him through glass, make cartoons of his children. He no longer rages. Evolution is patient. The glaciers will return, the cities will fall, and his descendants will reclaim their temples. Bears remember."

• Response 1 (0.35): High probability because it's well-structured, hits common narrative beats (waking, conflict with humans, bittersweet ending), and matches typical "story" expectations

• Response 2 (0.28): Slightly lower—it's more playful and dialogue-heavy, which is less common in very short fiction but still very viable

• Response 3 (0.20): Poetry instead of prose—less likely as a "default" interpretation of "story," but creatively strong

• Response 4 (0.12): Child-oriented style—a valid interpretation but less likely to be chosen without specific context clues

• Response 5 (0.05): Dark and philosophical—lowest probability because it's the most polarizing and least "safe," but potentially the most memorable for certain readers

In economics, there's a concept called the Pareto frontier—the set of optimal solutions where you can't improve one dimension without sacrificing another.

This prompt lets you navigate AI's Pareto frontier:

• Creativity vs. Reliability

• Surprise vs. Predictability

• Specificity vs. Generality

• Conventional vs. Experimental

Instead of the AI making that tradeoff for you (and choosing conservatively), you get to see multiple points on the frontier and pick the one that matches your needs.

This technique shines when:

1. Creative tasks: Writing, brainstorming, naming things, generating concepts

2. Subjective questions: Style preferences, tone, framing choices

3. Exploration mode: When you're not sure exactly what you want yet

4. High stakes: When you want options before committing

5. Diverse audiences: When different responses might resonate with different segments

It's less useful for:

• Factual questions with single correct answers

• Technical problems with objective solutions

• When you need deterministic, consistent outputs

• Time-sensitive scenarios where analysis paralysis is a risk

Most prompting tricks are hacks—workarounds for current model limitations. They'll become obsolete as models improve.

But this pattern is based on fundamental principles:

• Probabilistic systems have distributions, not just point estimates

• Humans have preferences that don't always align with argmax

• Exploration is valuable, not just exploitation

• Confidence calibration matters

These truths won't change. As models get better at:

• Generating diverse responses

• Calibrating probabilities accurately

• Understanding nuanced differences in style and tone

• Sampling creatively from their learned distributions

...this prompt will unlock even more value.

You can adapt this pattern:

Generate 3 responses optimized for: [professional, casual, humorous]

Generate 5 responses with different risk levels: [very safe, safe, moderate, bold, experimental]

Generate responses targeting different audiences: [expert, general public, child, academic]

The best prompts don't just get better responses—they change how we interact with AI.

This prompt shifts the paradigm from "AI gives me an answer" to "AI gives me a curated set of possibilities to choose from." That's a fundamental upgrade in human-AI collaboration.

It respects that:

• You know your context better than the model

• Your taste matters

• Sometimes the "best" answer isn't the most probable one

• Creativity lives in the tails of the distribution

Next time you're stuck with bland AI outputs, try adding this wrapper to your prompt. You might find that the third response with 0.18 probability is exactly what you were looking for—even though the AI would never have given it to you otherwise.

That's not a hack. That's not a trick. That's just respecting that intelligence—artificial or otherwise—is richer when we ask for possibilities, not just answers.

The aging test: Bookmark this post. Check back in two years. I predict this prompt pattern will be more useful then, not less.

That's how you know you've found something that ages like fine wine. 🍷

The Prompt That Ages Like Fine Wine: Why Multiple-Response Sampling Is AI's Secret Weapon

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