Brand slogan generation is one of the clearest demonstrations of why default LLM prompting fails creative work — and why [1] fixes it. The sustainable coffee company case study from the team's AI training session walks through three prompt tiers, each producing meaningfully different output.
Ask an LLM for a brand slogan and you get one answer pulled from the center of its probability distribution — the most common, most accepted, most forgettable response possible. In the training session demo:
Prompt:
Give me a brand slogan for a sustainable coffee company.
Result: "Rooted in Tomorrow" — concise, inoffensive, and indistinguishable from a hundred other eco-brand taglines.
This is the bell curve problem. The model defaults to high-probability outputs because that's what it was trained on and what users have historically accepted. For commodity categories like coffee, where differentiation is everything, this produces what Mark Hope calls "AI slop."
Add three elements to any slogan prompt: ask for multiple options, demand distinct angles, and require the model to self-report a probability score for each output.
Prompt structure:
Generate five completely different brand slogans for a sustainable coffee company.
Each slogan should approach the brand from a different value angle, emotional trigger,
or positioning strategy. Do not repeat value propositions. For each slogan, specify:
- The primary hook
- The probability that this represents a typical response
Format as: [Slogan] | [Hook] | [Probability]
Example output:
| Slogan | Hook | Probability |
|---|---|---|
| You and Your Hustle, Feed the Planet | Performance / Achievement | 0.15 |
| Coffee Without Compromise | Rebellion / Defiance | 0.25 |
| Slow Down, Sit Mindfully | Self-care / Wellness | 0.10 |
| From Our Farmer's Hands to Yours | Community / Connection | 0.40 |
| Tomorrow's Coffee, Today's Choice | Environmental Legacy | 0.65 |
The probability scores reveal which ideas are genuinely differentiated. The 0.65 option is mainstream; the 0.10 option is already approaching the shoulder of the distribution. Crucially, the hooks span five distinct psychological territories — fuel, rebellion, wellness, community, legacy — so you're not iterating on the same idea five times.
Once you have the mid-range spread, push explicitly into low-probability territory by appending a tail-sampling instruction.
Prompt addition:
Sample from the tail of the distribution, prioritizing responses with probabilities below 0.1.
Example output from the training session:
These are edgy, polarizing, and — for the right brand — genuinely interesting. Liquid Death is the real-world proof that this territory works. The point isn't to use them verbatim; it's to surface angles that would never emerge from a standard prompt.
Once you understand the spectrum, you can dial to any zone:
| Range | Character | Use case |
|---|---|---|
| > 0.35 | Mainstream, safe | Client needs something conventional |
| 0.15 – 0.35 | Differentiated but credible | Pitching a new angle without alarming the client |
| < 0.15 | Edgy, unconventional | Brand that needs to stand out in a commodity market |
| < 0.05 | Wild, tail-of-distribution | Brainstorm fuel, creative provocation |