These prompts help researchers apply AI at every stage of a desirability study — from preparing the word list to analyzing results and writing the final report. Each prompt includes placeholders for your specific study details. Paste them into ChatGPT, Claude, or any LLM and replace the bracketed sections with your data.
Prompt 1: Adapt the Microsoft reaction word list for a specific product
I am preparing a desirability study for [product type, e.g., "a mobile banking app targeting millennials"].
Here is the full 118-word Microsoft Product Reaction Card list:
[Paste the full word list here]
My brand-target words (the emotional qualities the design should communicate) are:
[List 3-5 brand-target words, e.g., "trustworthy, modern, simple"]
Please:
1. Select 25 words from the list that are most relevant for evaluating a [product type] — include words that cover the brand-target attributes, their opposites, and neutral reactions.
2. Ensure at least 40% of the selected words are negative or neutral.
3. Suggest 3-5 additional custom words not in the original list that are specific to the [industry/domain].
4. Flag any words whose emotional connotation might be ambiguous for [target audience] and suggest alternatives.
5. Present the final list in alphabetical order.Prompt 2: Analyze open-ended follow-up responses from a desirability study
I conducted a desirability study where [number] participants viewed [design description] and selected 5 words from a list of [number] reaction words. After selecting words, each participant answered: "Why did you choose these words?"
Here are all participant responses:
[Paste all open-ended responses]
Please:
1. Group responses into themes based on what aspect of the design drove the word choice (e.g., color palette, typography, layout, imagery, overall feel, content tone).
2. For each theme, list the specific words most commonly associated with it.
3. Identify any contradictions — participants who chose the same word for opposite reasons.
4. Highlight the 3 strongest patterns (themes mentioned by the most participants).
5. Note any surprising or unexpected reasoning that appeared in only 1-2 responses but could signal a meaningful minority perspective.Prompt 3: Generate a comparative desirability report
I tested two design alternatives in a desirability study. Each participant saw only one design and selected 5 words from a list of [number] reaction words.
Design A ([brief description]):
[Paste word-frequency table — word, count, percentage]
Design B ([brief description]):
[Paste word-frequency table — word, count, percentage]
Our brand-target words are: [list brand-target words]
Total participants: [number per design]
Please:
1. Identify the top 5 words for each design and note overlaps and differences.
2. Calculate and compare the brand-alignment score for each design (percentage of participants who selected at least one brand-target word).
3. Calculate the positive/negative ratio for each design.
4. Identify words that are strongly associated with one design but not the other (selected by >20% for one and <5% for the other).
5. Write a 2-paragraph summary of which design better communicates the intended brand personality and what emotional gaps remain.
6. Recommend 2-3 specific design adjustments for the weaker-performing design based on the word patterns.Prompt 4: Translate a desirability word list for a cross-cultural study
I am running a desirability study across [number] markets: [list languages/countries].
Here is my English word list ([number] words):
[Paste word list]
For each target language:
1. Translate each word, choosing the translation that preserves the emotional connotation in a product/design evaluation context (not just the dictionary definition).
2. Flag words where the emotional tone shifts significantly in translation and suggest an alternative that captures the intended sentiment.
3. Flag any words that are culturally inappropriate or meaningless in the target market and suggest replacements.
4. Maintain the same positive/negative/neutral balance as the English list.
5. Present the result as a table: English | [Language 1] | [Language 2] | Notes.