Wide-leg trousers and baggy jeans define the silhouette across Y2K streetwear with cropped tops and cargo pants. Soccer jerseys and blokecore are trending too.
Fashion moves weekly.
LLMs are trained yearly.
Your model knows fashion in general. It doesn’t know what this week looks like. Gist reads what creators are posting and wearing now, so your agent can understand and recommend what’s trending currently.
Know what’s trending now
Add current silhouettes, aesthetics, and patterns to every workflow.
Sample queries
Gist wk 37 · condensed sample
Flowing maxi dresses lead the week, especially in gingham, polka dots, and lightweight crochet. Fitted mini dresses remain visible, but the stronger signal is relaxed resort shapes with open necklines and textural fabric.
Make all your workflows dynamic
Adding fresh trends context improves product click-throughs across the board.
AI Shopping Agent
Ground recommendations in what shoppers are seeing and wearing this week.
AI Stylist
Build outfits around current silhouettes, pairings, and aesthetics.
E-commerce Search
Rank relevant products against the language and looks gaining momentum.
AI Curations
Keep smart merchandising edits aligned with live category demand.
Automated Synonyms
Map emerging fashion terms to the products already in your catalog.
Catalog Enrichment
Add current aesthetics, cuts, colours, and occasions to product metadata.
Set up a topic to get fresh context weekly, or query it live
Start with global fashion context or query for a specific audience or category.
Pre-Generated
A pre-generated, cached block with zero request-time latency. Fetch it on your schedule and give every response the same current fashion baseline.
GET /v1/context/fashionRequestcurl
curl https://gist-api.the-alt.co/v1/context/fashion \
-H "Authorization: Bearer $GIST_KEY"Response200
- topic
- fashion
- segment
- Global
- knowledge cutoff
- Aug 16, 2026
- read
- A relaxed, wide-leg silhouette is dominant across the week, pairing baggy jeans and wide-leg trousers with fitted, cropped tops…
FAQs
How fresh is the fashion context?
The source index and pre-generated fashion block refresh weekly. Every response includes a knowledge cutoff and date window, so your product always knows exactly what period it is using.
What is the difference between Pre-Generated and Live Query?
Pre-Generated gives every workflow the same cached weekly baseline with no request-time generation. Live Query searches the weekly index for a specific category, region, audience, colour, cut, or other property.
Does a live query crawl the web while my user waits?
No. Queries run over social signals that Gist has already collected and indexed. Repeat queries are served from cache, so there is no added crawl in the consumer response path.
What happens when there is not enough evidence?
The response returns noSignal when the corpus cannot support an answer. Your product can then fall back to its existing logic instead of receiving an invented trend.
Can I query a specific market or category?
Yes. Live Query is designed for narrower questions across regions, demographics, sub-categories, garment types, colours, cuts, and other fashion properties.
Where should I use the context in my stack?
Use the weekly block in a system prompt, ranking feature, merchandising process, or enrichment job. Use Live Query when the shopper or workflow asks a question the shared baseline cannot anticipate.
Add what’s current to what your model already knows.
Give every fashion workflow a weekly fresh baseline and a way to ask deeper questions.