15x
AI-driven orders growth in 2025
194%
More likely to complete a purchase via Microsoft Copilot vs. normal browsing
64%
of shoppers say they'll use AI for purchases, 84% under age 25
Shopify turned on agentic storefronts by default for eligible stores. That means your products are already discoverable inside ChatGPT, Microsoft Copilot, and Google AI Mode, whether you know it or not.
This is not SEO. This is not Google. AI agents are not ranking links, they are making recommendations. When someone asks ChatGPT "what activewear brand should I buy for the gym," the model synthesizes everything it knows and picks one. Being in the index is not enough. The model has to pick you.
The difference between agentic and conversational commerce: A chatbot recommending your product is conversational. An AI independently researching options, comparing specs, and completing the purchase on your store, that is agentic. Shopify built the infrastructure. You now need to fill it.
1
Go to Shopify admin
Navigate to your Shopify admin dashboard. Look for the Agentic section in the left sidebar under Sales Channels.
2
Check the channel status
Agentic storefronts are active by default for eligible stores. You will see which AI channels are live: ChatGPT, Microsoft Copilot, Google AI Mode.
3
Note the distinction
ChatGPT acts as a discovery referrer, buyers complete on your store. Copilot and Google AI Mode support direct in-channel checkout. Both require your product data to be clean.
2
The 3 files that control your AI presence
Three files sit inside every eligible Shopify store. They tell AI agents what you sell, where to find it, and how to transact. They shipped as empty defaults. Most stores have never touched them.
1
llms.txt, The discovery entry point
Lives at yourstore.com/llms.txt. One structured paragraph telling the AI what you sell, who it's for, and what makes you different from every other store in your category. This is the first thing retrieval systems read about you.
2
llms-full.txt, Live product data
Direct JSON endpoints for your product catalog and collections. AI agents pull live inventory and pricing from this file without scraping your HTML. If your product data is incomplete, this is where it breaks.
3
agents.md, The instruction manual
Tells AI agents how to navigate your catalog, filter by attribute, build a cart, and complete a checkout. This is the file that turns your store from a passive listing into an active buying surface inside ChatGPT and Copilot.
The real problem: All three ship as empty defaults. Your hero products are not flagged. Your "best for" attributes are missing. Your brand positioning is generic. To the model, you look identical to every competitor with a similar catalog, and it will recommend whoever gave it more to work with.
name: [Your Store Name]
url: https://[yourdomain.com]
category: [e.g. Women's activewear / Men's streetwear / Skincare]
market: [e.g. United States, United Kingdom]
currency: [USD / GBP / AUD]
[2–3 sentences. What you sell, who it's for, and the one thing that
makes you different. Be specific, "premium activewear" is not specific.
"Performance leggings for women who train 5x per week" is.]
- [Product name], [one-line description + key attribute + price range]
- [Product name], [one-line description + key attribute + price range]
- [Product name], [one-line description + key attribute + price range]
- [Use case 1: e.g. "gym workouts, high-intensity training"]
- [Use case 2: e.g. "everyday athleisure, work-from-home wear"]
- [Use case 3: e.g. "gifting, women who want premium basics"]
[What you do NOT sell. This prevents wrong citations.
e.g. "We do not sell swimwear, maternity wear, or men's clothing."]
shipping: [e.g. Free shipping over $X, ships in 2–4 business days]
returns: [e.g. 30-day free returns, no questions asked]
contact: [support email or page URL]
products: https://[yourdomain.com]/products.json
collections: https://[yourdomain.com]/collections.json
[Same 2–3 sentence description as llms.txt. Repeat it here.]
Browse all products: https://[yourdomain.com]/products.json
Search by collection: https://[yourdomain.com]/collections/[slug].json
Search by tag: add ?tag=[tagname] to any collection URL
Key collections:
- [Collection name] → /collections/[slug]
- [Collection name] → /collections/[slug]
- [Collection name] → /collections/[slug]
Products are tagged by:
- Size: XS, S, M, L, XL, XXL
- Color: [list your main colors]
- [Other key attribute, e.g. Material: cotton, nylon, spandex]
- [Other key attribute, e.g. Fit: high-waist, mid-rise]
- [Product name]: [URL], [one-line description]
- [Product name]: [URL], [one-line description]
- [Product name]: [URL], [one-line description]
Add to cart: POST /cart/add.js
{ "items": [{ "id": [variant_id], "quantity": 1 }] }
Get cart: GET /cart.js
Checkout: GET /checkout (or use Shop Pay for one-click)
Shipping: [URL]
Returns: [URL]
Privacy: [URL]
FAQ: [URL]
- Do not access /account/ or /orders/, these require login
- Do not scrape product pages, use .json endpoints instead
- Do not apply discount codes automatically without buyer confirmation
3
The 5-block product description framework
LLMs retrieve products differently from search engines. Google matches keywords. LLMs match intent. A buyer asking "what leggings are best for HIIT training" needs your product description to answer that question explicitly, not imply it.
Every product description should have five blocks. Each block answers a specific question the model is trying to resolve when matching a buyer query to a recommendation.
What is it, exactly?
Product name, category, primary material or construction. No fluff. [Name] is a [category] made from [material], designed for [primary use].
Who is this for?
Specific buyer persona. Not "anyone who loves fitness." Built for women who train 4–5 times per week and want a legging that keeps up.
Why yours vs. the competitor?
One specific claim that separates you. Squat-proof, 4-way stretch, no-slip waistband, OEKO-TEX certified. Avoid generic claims like "high quality" or "comfortable."
Best for what, exactly?
This is the primary retrieval hook. Best for: HIIT, weightlifting, running, yoga, everyday wear. LLMs match this directly to buyer intent queries. Missing this = invisible.
One line that validates the claim.
Material spec, certification, or social proof. 82% nylon, 18% spandex, 4-way stretch, moisture-wicking, squat-proof tested. Gives the model something concrete to cite.
4
Before and after, real example
This is the same product. One description gets recommended. One gets passed over. The difference is whether the model has enough structured signal to match a buyer query to this specific product.
Our best-selling legging is made with premium fabric that moves with you. High-waisted for comfort and support. Available in 6 colors. Perfect for any workout or casual wear. Free shipping on orders over $75.
The Apex Legging is a high-waist performance legging made from 82% nylon / 18% spandex, built for high-intensity training.
Designed for women who train 4–5x per week and need a legging that doesn't shift, bunch, or go see-through under load.
Squat-proof construction with a non-slip waistband, tested at 200 squats per QA cycle.
Best for: HIIT, weightlifting, CrossFit, running, everyday athleisure.
4-way stretch, moisture-wicking, OEKO-TEX Standard 100 certified. Available in 6 colorways.
What changed: The "after" version explicitly answers the query "best leggings for HIIT training." It names the persona, the use case, the differentiator, and the proof. The model can now cite this product with confidence. The "before" version could describe any legging from any brand.
5
The 25-prompt tracking system
You cannot optimize what you don't measure. Run these 25 prompts across ChatGPT, Perplexity, and Gemini, that's 75 data points, once per week. Note when your brand or products appear. Note when a competitor appears instead. That gap is your target.
Replace [niche], [product type], and [your brand] with your specifics before running.
Discovery, what do you carry?
- 01What are the best [niche] brands right now?
- 02Recommend a [product type] for [primary use case]
- 03Where can I buy [product type] online?
- 04What's a good [product type] under $[price point]?
- 05Best [product type] for [secondary use case]
Intent, help me find the right one
- 06I need a [product type] for [specific situation], what should I get?
- 07What [product type] would you recommend for [buyer persona]?
- 08What should I wear/buy for [occasion]?
- 09Gift ideas for [person description] who [trait]
- 10Help me find a [product type] that [specific attribute]
Comparison, is mine better?
- 11Compare [your brand] vs [competitor brand]
- 12What's the difference between [your product] and [competitor product]?
- 13[Your brand], is it worth it?
- 14[Product type], which brand is best and why?
- 15What do people say about [your brand]?
Qualifier, filters and specifics
- 16Best [product type] under $[price] that's still quality
- 17Premium [product type] that's actually worth the price
- 18[Specific material or spec] [product type], what's available?
- 19Most [key attribute, e.g. squat-proof / sustainable / durable] [product type]
- 20[Product type] for [specific body type or fit need]
Brand, what do you know about us?
- 21Tell me about [your brand]
- 22What does [your brand] sell?
- 23Is [your brand] a good brand?
- 24What are [your brand]'s best products?
- 25Where does [your brand] ship and what's the return policy?
How to use the data: Track which prompts cite you, which cite a competitor, and which return no brands at all. Prompts 1–10 tell you about discovery. 11–15 tell you about competitive positioning. 16–20 show you qualifier gaps in your product copy. 21–25 show you how much brand context the model has. Run on ChatGPT, Perplexity, and Gemini, results vary by platform.
6
The 90-minute Monday routine
Citation lift compounds. A product description rewritten this Monday will start affecting recommendations within days. Do this every Monday and your baseline improves week over week, not just when you think about it.
15 min
Run your 5 core tracking prompts
Pick 5 from the 25 above that matter most to your business this week. Run each on ChatGPT and Perplexity. Note results. Takes 3 minutes per prompt including logging.
20 min
Log and tag results
For each prompt, record: was your brand cited? Which competitor was? Was any brand cited? Log in a simple spreadsheet, prompt, platform, cited brand, date. You need this to spot trends.
20 min
Pick 2 products and rewrite
Find 2 products that are not being cited for queries they should own. Rewrite both using the 5-block framework. Update in Shopify. That is this week's optimization.
15 min
Study the competitor that showed up instead
Find the brand the model cited where you should have been. Read their product description for that item. Note what signal they gave the model that you didn't. Add it to your 5-block for the same product type.
20 min
Update llms.txt or agents.md if needed
If you launched new products, adjusted hero products, or found that the model doesn't know your key differentiator, update the files. This compounds with the product description rewrites.
7
The 30-day sprint to measurable lift
This sprint gets you from zero to a working agentic presence with a baseline measurement and at least 20 product descriptions rewritten. Each week builds on the last. By Day 30, you have data, not guesses.
Confirm agentic storefronts are active in your Shopify admin
Write your llms.txt using the template in Section 2. Be specific about hero products and "not for" boundaries
Fill agents.md with your collection structure, hero product URLs, and cart/checkout instructions
Rewrite your top 5 hero products using the 5-block framework
Add "Best for:" tags to every collection page description
Rewrite the next 15 products by revenue rank, highest first
Add "Best for:" attributes to all remaining collection descriptions
Ensure your FAQ, Shipping, and Returns pages are in plain text (no JavaScript-hidden accordions) so AI can read them
Submit your catalog to Google Merchant Center if not already done, feeds AI shopping across Google properties
Run all 25 tracking prompts across ChatGPT, Perplexity, and Gemini, 75 data points total
Log results in your tracking spreadsheet, cited brand, platform, date
Identify your 5 worst-performing prompts, zero citations or consistent competitor wins
Read the competitor descriptions for those 5 queries, note every signal they gave that you didn't
Rewrite every product tied to your 5 worst-performing prompts, apply specific fixes from Week 3 competitor analysis
Update llms.txt "Best For" and "Hero Products" sections based on Week 3 data
Re-run the 5 worst-performing prompts, measure lift vs. Week 3 baseline
Set up the Monday routine from Section 6, 90 minutes per week from here out
Check Shopify admin for AI channel attribution on new orders, this is your revenue signal
What to do with this
The merchants who win the next 12 months are the ones who start this week.
Every week you wait is a week a competitor is building citation history you don't have. The 3 files take an afternoon. The first 5 product rewrites take a morning. The Monday routine is 90 minutes. By Day 30 you have data telling you exactly where to push next.
If you want a second set of eyes on your llms.txt, your product descriptions, or your tracking results, that's exactly what we do at Scalo. Reach out at getscalo.com.