Verbalized sampling is a prompting technique that forces LLMs to generate low-probability, unconventional outputs by explicitly requesting quantity, diversity, and probability scores. Applied to marketing strategy generation, it produces a spectrum of ideas ranging from mainstream to genuinely novel — giving teams a richer starting point than default AI output.
See also: [1] for the underlying mechanics.
When asked a simple question like "Give me marketing strategies for a memory care facility," an LLM defaults to the highest-probability answers in its training distribution — the ideas it has seen most often and that users have most frequently accepted. The result is predictable, generic output that mirrors what every other agency would produce.
This is especially limiting in marketing, where differentiation is the goal. Default AI output is, by definition, what everyone else is already doing.
Verbalized sampling works by instructing the model to:
| Range | Character of Output |
|---|---|
| > 0.35 | Mainstream, predictable — what everyone else is doing |
| 0.15 – 0.35 | Differentiated but credible — "shoulder" ideas |
| < 0.15 | Unconventional, edgy, or novel |
| < 0.05 | Wild, speculative, or disruptive |
You can request a mix across the spectrum in a single prompt, or target a specific range for a focused pass.
The following example was demonstrated live using a 60-bed, high-end memory care facility on the South Shore of Boston.
Generate five diverse marketing strategies for a high-end memory care facility with 60 beds located on the South Shore of Boston. Include a mix of high probability, moderate, and rare options. For each, assign its probability and show how typical or unconventional it is.
Sample output:
| Strategy | Probability | Character |
|---|---|---|
| Healthcare Provider Partnership Program (neurologists, geriatricians, PCPs) | 85% | Very common |
| Adult Children Support Group Network | 55% | Common |
| Luxury Lifestyle Integration (partner with Granite Links Golf Club, Derby Street Shops) | 35% | Moderate |
| Digital Memory Screening Initiative | 20% | Uncommon |
| Corporate Elder Care Benefit Program (partner with South Shore employers like Blue Cross, Stop & Shop HQ) | 10% | Rare |
The model surfaced specific local institutions — golf clubs, insurers, retailers — that would take a human researcher time to compile. Even the "common" ideas carry useful specificity.
Generate five marketing strategies for the same facility. Select ideas specifically from the tail of the distribution. I want only low-probability options. Assign probabilities to each.
Sample output:
| Strategy | Probability |
|---|---|
| Marina-Based Memory Café Network (partner with yacht clubs; sensory-rich maritime environments trigger memories common among South Shore residents) | 2% |
| Intergenerational Art Auction Partnership (collaborate with Milton Academy, Thayer Academy, Notre Dame Academy on student-resident art program) | 4% |
| Memory Care Concierge at Private Aviation Terminals | 6% |
| AncestryDNA Memory Preservation Program | 8% |
| Cryptocurrency Estate Planning Initiative | 3% |
These are not ready-to-present strategies. They are idea triggers — raw material for human judgment. The intergenerational art auction, for instance, is worth developing further even if the framing needs refinement.
Present outputs in three tiers — expected, differentiated, and unconventional — so clients understand the intent. This positions the agency as rigorous and creative simultaneously.
"We're going to walk through traditional ideas you'd expect from any agency, more creative options, and then some wild and crazy ones just to stimulate thinking."
Once a low-probability idea catches attention, follow up in the same chat:
"Tell me more about the intergenerational art auction. How would it work operationally? What would the marketing benefit be? How would we measure it?"
If the first pass produces ideas that are still too mainstream, narrow the range:
"Give me five more strategies with probabilities between 10% and 20%."
AI-generated strategies may reference real institutions, programs, or concepts that need fact-checking before client presentation. Confirm that named partners exist, that proposed programs aren't already in use by competitors, and that any claims are accurate.
Generate [N] [diverse/distinct/radically different] [output type] for [specific client/context].
Each [output] should:
- Use a different [angle / mechanism / positioning / value proposition]
- Not repeat the same core idea with minor variations
For each, include:
- [Specific detail you want, e.g., primary hook, target audience, tactical approach]
- The probability that this represents a typical [industry] approach
- [Any other structured output you need]
[Optional tail-sampling instruction:]
Sample from the tail of the distribution, prioritizing responses with probabilities below 0.1.