Verbalized Sampling — Marketing Strategy Generation

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.

The Problem with Default AI Strategy Output

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.

Core Technique: Verbalized Sampling

Verbalized sampling works by instructing the model to:

1. Generate quantity — ask for multiple items (e.g., five strategies), not one
2. Enforce diversity — require each item to use a different angle, mechanism, or framing
3. Assign probabilities — ask the model to score how likely each output would be without the prompt
4. Target the tail — optionally constrain outputs to low-probability ranges

Probability Ranges as a Dial

You can request a mix across the spectrum in a single prompt, or target a specific range for a focused pass.

Applied Example: High-End Memory Care Facility

The following example was demonstrated live using a 60-bed, high-end memory care facility on the South Shore of Boston.

Prompt Pattern 1 — Mixed Probability Spread

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:

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.

Prompt Pattern 2 — Tail Sampling Only

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:

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.

How to Use This in Client Work

Frame it as a structured brainstorm

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."

Drill down on promising outliers

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?"

Iterate by probability band

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%."

Always verify

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.

Key Prompt Elements (Reusable Template)

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.

Tool Notes

• Claude tends to produce more creative, "out-there" outputs than ChatGPT for this technique — preferred for tail-sampling passes
• ChatGPT is more conservative and may resist very low-probability requests
• Start a new chat for each new client or topic — retained context from prior prompts will bias outputs

Related

• ^[1]
• ^[2]
• ^[3]
• ^[4]
• ^[5]