Examples
Product description examples for clothing
A clothing description has to replace the fitting room: how it fits, what it is made of and how to look after it. Below are seven before-and-after descriptions from different stores (a boutique, a denim brand, a German knitwear line, kids' outerwear, plus sizes), the fashion title patterns that go with them, and how to produce them across a whole catalog.
We connect your store, build the feed and keep it in sync. Updated 2026-10-27.
At a glance
- Fit first: cut (slim, relaxed, oversized), where it sits (rise, length, drop shoulder) and a reference point such as the model's height and size worn.
- Fabric second: fiber percentages, weight or weave, stretch and how it feels; "100% cotton" alone does not tell a shopper whether it is a tee or a canvas jacket.
- Care last but always: wash temperature, drying and ironing in one line. It answers the question that otherwise turns into a return.
- The same three facts feed Google Shopping and Meta, so they belong in structured fields (material, fit, care) that a template or AI config can reuse.
Product description examples for clothing, line by line
| Element | Weak line | Strong line |
|---|---|---|
| Fit | Flattering fit for everyone. | Relaxed through the body with a dropped shoulder; size down for a closer fit. Model is 175 cm and wears S. |
| Fit (pants) | Great jeans. | Slim taper, mid rise (26 cm), 81 cm inseam on size 32. |
| Fabric | Premium material. | 100% European linen, garment-washed so it is soft from the first wear. |
| Fabric (stretch) | Comfy and stretchy. | 98% cotton, 2% elastane denim, 12 oz: holds its shape, gives at the knee. |
| Care | Handle with care. | Machine wash at 30 °C, line dry, warm iron while damp. |
| Occasion | Perfect for any occasion. | Light enough for a heatwave commute; layers under a blazer in spring. |
Brands, measurements and fabrics in these examples are illustrative.
Product title examples for fashion
A description can run to hundreds of words; the title has to say the same thing in a line. In Google Shopping the start of the title is what shows, so the attributes shoppers filter on come first. These patterns match the descriptions below.
| Product | Pattern | Title |
|---|---|---|
| Shirt | Brand + gender + product + fabric + color + size | Marlow Women's Relaxed Linen Shirt, Sand, S |
| Jeans | Brand + gender + fit + product + fabric + wash + size | Hartfield Men's Slim Taper Jeans, 12 oz Stretch Denim, Indigo, 32x32 |
| Knitwear | Brand + product + fiber + color | Nordvik Merino Crew-Neck Sweater, Charcoal |
| Kids | Brand + kids + product + feature + age size | Puddle Kids' Waterproof Rain Jacket, Yellow, 5-6Y |
| Activewear | Brand + gender + product + feature + length | Kinetik Women's High-Rise Leggings, Squat-Proof, 7/8 |
Where clothing descriptions go wrong
- Adjectives instead of facts: soft, premium and flattering are claims; fiber content, weight and cut are evidence.
- No size reference: without the model's height and size, every fit statement is a guess, and guesses come back as returns.
- Care missing from the feed: the store page shows care icons, but the description in the feed is text only, so the icons never reach Google Shopping or Meta.
- HTML in the feed: size tables and bullet tags from the store editor arrive as raw markup. Channels want plain text.
Examples
Worked examples, by situation
Each example is a different store and a different kind of garment. The before is what the store had; the after covers fit, fabric and care.
Linen shirt: from mood to measurements
Before
Our favourite shirt for summer! Light and breezy, you'll live in it.
After
A relaxed linen shirt with a dropped shoulder and a curved hem that sits at the hip. Cut roomy: our model is 175 cm and wears S; size down for a closer fit. 100% European linen, garment-washed for a soft hand from day one. Machine wash at 30 °C, line dry, iron warm while damp.
How we set it up
Fit, fabric and care live in three metafields. We set up an AI config that enhances the description, reading title and description with the three metafields as additional context, and map description with replace from AI config, falling back to the store description.
Jeans: numbers a shopper can compare
Before
Classic slim jeans in a dark wash. A wardrobe essential.
After
Slim taper jeans with a mid rise (26 cm on size 32) and an 81 cm inseam. 12 oz denim, 98% cotton and 2% elastane: structured, with give at the knee. Dark indigo rinse that softens with wear. Wash cold inside out, hang dry to keep the color.
How we set it up
We map description with an advanced rule: "Slim taper jeans with a " + [rise] + " and a " + [inseam] + " inseam. " + [fabric] + ". " + [care]. Rise and inseam come from a merged source (a size chart sheet keyed on SKU), so every variant states its own numbers; every variant needs a row in the sheet, or its description mapping fails.
Merino sweater translated, care included
Before
Merino crew neck. Fine knit. Charcoal.
After
Feiner Rundhalspullover aus 100 % Merinowolle, 18,5 Mikron, gerade geschnitten mit leicht überschnittener Schulter. Wärmt, ohne aufzutragen, und kratzt nicht. Pflege: Handwäsche oder Wollprogramm bei 30 °C, liegend trocknen.
How we set it up
We set up an AI config that enhances the description with target language German, reading title and description plus fiber and care attributes as additional context. description uses replace from AI config; the German feed uses accepted generations only.
Leggings: the claim backed by the fabric
Before
Squat-proof leggings you'll never want to take off!
After
High-rise 7/8 leggings with a wide waistband that stays put. 75% recycled nylon, 25% elastane, double-knit and opaque when you bend. Compressive but not tight at the ankle. Wash at 30 °C, no fabric softener, which breaks down the stretch.
How we set it up
We map description as a combine of [fit_note], [fabric] and [care] with a space as delimiter, so the feed version leads with fit and fabric in the first line that ad placements show. The store keeps its longer story-led copy.
Rain jacket: sizing for growing children
Before
Bright yellow raincoat for little adventurers.
After
Waterproof rain jacket with taped seams and a fixed hood. Cut with room to layer a sweater underneath; size 5-6Y fits 110-116 cm. Recycled polyester shell with a fleece-free mesh lining that dries fast. Wipe clean or machine wash at 30 °C, hang to dry.
How we set it up
We map description with an advanced rule that appends a size line: [description] + " Size " + [size] + " fits " + [height_range] + "." The height range comes from a Google Sheet merged on size, so the size chart lives in one place; every size in the catalog needs a row there, or the expression fails for that product.
Wrap dress: fit described across the size range
Before
Our bestselling wrap dress, now in sizes 14-28.
After
A true wrap dress with an adjustable tie, so the waist sits where you want it. Midi length: 117 cm on size 18, falling below the knee. Fluid viscose crepe, 160 gsm, with enough weight to skim rather than cling. Model is 170 cm and wears 18. Wash at 30 °C on a gentle cycle, cool iron.
How we set it up
We set up an AI config that enhances the description, with length per size, fabric weight and model details as additional context, used through replace from AI config: only accepted generations are used, so an unreviewed dress keeps its store description until someone approves the new one.
Care instructions from one lookup sheet
Google Sheet: care_by_fabric
fabric_code,care
LIN100,Machine wash 30 °C. Line dry. Warm iron while damp.
MER100,Hand wash or wool cycle 30 °C. Dry flat.
COT-EL,Wash cold inside out. Hang dry.How we set it up
We join the care sheet as a merged source on fabric_code, which adds a care column to every variant, and map description as a combine of [description] and [care] with a space. Forty rows maintain care text for six thousand variants, and a new fabric code shows up as an empty care value in feed hygiene's completeness check.
Copy this plan
Plan: clothing descriptions for a whole catalog
Get the facts into fields first; the writing, by template or AI, comes after.
01
Audit which fit, fabric and care facts you have
Check how many products have material, size chart and care data filled, and whether the values are spelled consistently.
What we set up: We run feed hygiene's completeness and value consistency checks on your catalog.02
Fill the gaps from spreadsheets you already keep
Size charts and care labels usually exist in a production sheet. Join them by SKU or fabric code instead of retyping.
What we set up: We join your Google Sheet as a merged source, keyed on SKU or fabric code.03
Choose template or AI per product line
Spec-led lines (denim, basics) suit an advanced template; fashion pieces with a story suit an AI config that reads the same facts as context.
What we set up: We write an advanced description template for spec-led lines and an AI config that enhances descriptions for the rest.04
Review the AI text before it ships
Read a sample, accept what is right, regenerate what is not. Only accepted descriptions are used in the feed.
What we set up: We go through the generations with you, accepting them one by one or all at once.05
Send the result to each channel
Map description from the AI config with the store description as fallback, then publish to Google Shopping and the Meta catalog.
What we set up: We build the Google Shopping and Meta Ads feeds and map description with replace from AI config.
Customization tips
Make it yours
Model height beats size-chart links
A line like "model is 175 cm and wears S" answers more fit questions than a link to a size chart, and it survives in a text-only feed. It does require a photo shoot note per product, which small brands often skip.
Template for basics, AI for hero pieces
A template guarantees every variant states its rise, inseam and fabric correctly. AI writes better prose but reads only what you give it, so a fact missing from the fields will be missing from the text.
One care line, not a paragraph
Care text is the most repeated part of a clothing description. Keep it to one line from a lookup so it stays consistent and does not crowd out fit and fabric.
Translated copy needs translated sizes
A German or Swedish description that says "fits US size 8" confuses the market it is for. Put EU sizes and centimeters in the source fields before translating.
FAQ
Questions about product description examples for clothing
What should a clothing product description include?
Fit (cut, rise or length, and a reference such as the model's height and size worn), fabric (fiber percentages, weight or weave, stretch) and care (wash temperature, drying, ironing). Styling comes after those facts.
How long should a clothing product description be?
Long enough to state fit, fabric and care, usually 50 to 150 words on the product page. Google Shopping accepts up to 5,000 characters and Meta up to 9,999, but the first sentence carries the most weight in ads.
What is a good product title for fashion?
Brand, gender, product type, the defining feature (fit, fabric or cut), color and size, in roughly that order, for example "Marlow Women's Relaxed Linen Shirt, Sand, S". Put the attributes shoppers filter on near the start.
Can AI write clothing descriptions for a whole catalog?
Yes, if the facts are in the data. An AI config reads the title, description and any extra attributes you pass as context, such as fabric and care, writes a description for each product, and a person reviews and accepts the results before they reach the feed.
Set up for you
Have every garment described by fit, fabric and care.
Send us your store and your size and care sheets. We join them to the catalog in Textfinity, and its AI configs generate descriptions that you review before they ship.
Tell us your store and channels. We reply within 24 hours.