Feeding References to AI
Once you can see what's good, the next step is making AI know it too. This lesson covers three ways to feed references into the chat: reference images, style descriptions, and design variables—with prompt templates ready to copy.
"A bit warmer, a bit more premium"—say it ten times, nine warp inside the model. Adjectives take two translations: you compress feeling into words, the model expands words into pictures; both ends lose fidelity. Feed references straight to AI—least loss, one shot.
Three postures for feeding references—each covers a stretch. Pick from the table:
| Method | What you give | Best for | Weak spot |
|---|---|---|---|
| Reference image | 1–3 reference images | Overall vibe, composition, style transfer | Easy to copy layout too |
| Style description | A spec paragraph: density, primary, radius | Styles you can state as rules | Vibe you can't spell out stays unwritten |
| Design variables | Token list: primary / radius / type / spacing | Having AI write UI code | Locks style, not layout or copy |
You don't invent style descriptions from scratch—there's a method. About Face 4, Chapter 17, records Cooper's practice: experience attributes. Before designing, pick 3–5 adjectives with the client that describe product vibe and brand promise—"clean, restrained, trustworthy." Once set, those words referee every visual call: when unsure, ask whether the words would approve.
Adjectives are allowed to fight. The book says "safe" and "flexible" can both sit at the table—keep that tension: where two words clash is exactly what early style drafts should answer first.
Moved to feeding AI, it fits flush: pin the vibe with adjectives, then translate each into a concrete spec. Adjectives alone, AI reads the average; finish the translation step and the style description is done. The workbench below does that translation.
Brief: home for a budgeting app, vibe locked as "clean, restrained, trustworthy." Version A sends the three words as-is; B runs the workbench first, translates word by word into specs, then sends. Tap the one you think is better.
Each feed method gets a ready prompt. Pick a method, copy, swap the placeholders, send. For style description, drop the workbench output straight into the body.
The third method deserves its own line. Tear a reference into design tokens—primary, radius, type, spacing each become a variable—and what you hand over is more than a reference: it's an interface standard.
Why are standards valuable? About Face 4, Chapter 17, cites Nielsen: a unified interface standard helps users learn faster and err less, because experience in one place predicts behavior elsewhere; for the team, ready decisions skip round after round of debate. The same ledger holds for AI: once the token list is in, every generation lands on the same standard—ten revisions won't drift, and you have a yardstick at review.
The same chapter says the hard part first: follow the standard unless you have a strong alternative. Breaking is allowed—reasons must be hard. That rule fits you and AI alike. One last duel settles this lesson's ledger on the spot.
Brief: brand page for a bakery studio. A's input is only "a bit warm, a bit premium"; B brings a reference image and a variable list. Tap the better version.
Adjectives warp; references don't. Stuff images, spec text, and variable lists straight into the chat—least loss.
Three feeds, three stretches: Reference images cover vibe, style descriptions cover rules, design variables cover code. Combine if you want—just know who owns what.
Style description has a method: Pick 3 adjectives to pin the vibe (experience attributes), then translate word by word into specs. Words referee; specs execute.
A token list sets the standard for AI: Value is predictability—every generation lands on the same variables, drafts don't drift, review has a yardstick.
Mediocre output? Check the input first. Adjectives alone land on the average; swap in references and variables and the same model changes face at once.
Source: Original to Xiaoshan Academy's Taste Engineering series; some design principles adapted from About Face 4, Chapter 17 (Alan Cooper et al.); experience attributes and standards discussion from the same chapter.