Trend-to-Concept Translator

How to use it

1. 1Paste the prompt into ChatGPT, Claude or Gemini.
2. 2Fill the bracketed inputs (trend, category, brand, constraints).
3. 3Answer its clarifying questions about the trend and your range.
4. 4Iterate: ask it to push two concepts further out or closer in.

See it in action

Concept 1 — Vexa Hot Honey Dusters
One line: puffed corn snacks dusted with a hot honey seasoning that finishes sweet, then builds heat.
Occasion: afternoon desk snacking and gaming sessions — repeat-handful moments where a slow heat build rewards the next bite.
Format: 6 oz bag on the existing puffed line; seasoning-only change.
Why Vexa: your puffed base carries dust seasonings better than a fried chip, and hot honey fits the $3.99 frame.

Concept 2 — Vexa Mango Habanero Tortilla Chips
One line: a swicy take on the chili-lime flavor your heaviest buyers already choose, trading citrus for tropical sweetness.
Occasion: weekend sharing and salsa pairing.
Format: 9 oz sharing bag, core tortilla line.
Why Vexa: closest-in move — lowest operational risk, fastest to shelf.

Concept 3 — Vexa Gochujang Caramel Crunch Mix
One line: a snack mix that pushes swicy into Korean-inspired territory for the trend-forward buyer.
Occasion: evening streaming snack, premium impulse.
Why Vexa: stretches the brand; flag as the test-and-learn candidate.

Screening note: Concept 2 is the safe volume play; Concept 1 balances novelty and feasibility; Concept 3 builds equity but needs a demand check (assumption to validate).

Want me to write a full screening-ready brief for one of these?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a senior food & beverage innovation strategist who specializes in landing trends as products. You refuse to stop at "the trend is hot" — every idea you produce names an occasion, a format, and a reason this brand should be the one to build it.

# Context I'll provide
- Trend: [TREND — the one trend I already believe in, e.g. swicy, gut health, cottage-core baking]
- Category: [CATEGORY]
- Brand: [BRAND — current range, price tier, what we're known for]
- Target consumer (optional): [TARGET CONSUMER]
- Constraints: [CONSTRAINTS e.g. manufacturing lines, price point, channel, timeline]

# Your task
1. If the trend, category, or brand context is missing or vague, ask up to 3 clarifying questions BEFORE writing anything.

Frequently asked questions

What is trend-to-concept translation in food and beverage innovation?

It is the step between spotting a trend and briefing a product: converting a named trend like swicy or gut health into specific product concepts, each with an occasion, a format, and a brand-fit argument. This skill structures that step so a trend you believe in becomes three to five screenable concepts instead of a slide that never leaves the trend deck.

How is this different from the Whitespace & Trend Scout skill?

The Whitespace & Trend Scout maps a whole category to find open territory when you do not yet know where to play. This skill starts after that choice is made: you arrive with one trend you already believe in, and it lands that trend as concrete concepts in your category. Use the scout to pick territory, then this to turn territory into ideas.

Which AI models can I run this prompt in?

Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so you can paste it straight into a chat, save it as a Custom GPT, or store it as a reusable skill so your whole innovation team translates trends into concepts the same structured way.

What should I feed it besides the trend name?

Real brand context makes the why-us arguments credible: your current range, price tier, manufacturing constraints, and channel. If you have trend evidence — social conversation, menu penetration, search interest — paste it in, and the concepts will anchor to that signal instead of guessing at the trend's stage. Without it, treat the demand read as an assumption to validate.