OpenAI’s new shopping research mode turns ChatGPT into a dedicated product-research agent that interviews you, crawls the web, and hands back a full buyer’s guide, with “nearly unlimited usage” for Free, Go, Plus, and Pro users through the holidays.

Separately, Google’s AI Mode shopping upgrade pairs Gemini with the Shopping Graph plus new “agentic” features—price-tracking that can auto-buy for you and even phone local shops—to make Search and the Gemini app feel like a visual, hands-on shopping concierge.

How does OpenAI's shopping research agent work? ‍

ChatGPT shopping research runs on a post-trained GPT-5 mini that’s tuned specifically for commerce: it asks clarifying questions about budget, who the item is for, and must-have features, then pulls fresh prices, availability, specs, images, and reviews from “quality sources” to build a ranked buyer’s guide. You steer it by marking items “More like this” or “Not interested,” and if Memory is on, it can reuse what it already knows about you (e.g., that you game on a 15-inch laptop) to bias recommendations.

By contrast, Google’s AI Mode shopping experience sits directly on top of the Shopping Graph—more than 50 billion product listings, with 2 billion refreshed every hour—so when you describe “cozy sweaters for happy hour in warm autumn colors,” it responds with a shoppable, highly visual grid, not just text. In the Gemini app, the same stack lets you go from “my sister is an NYC student who walks everywhere” to specific outfit or gift suggestions that respect style, weather, and budget in one chat thread.

On the “agentic” side, agentic checkout lets you track a particular SKU, set size, color, and a target price on a slider, then have Google watch across merchants; when it drops into your range, you get a notification and can approve a one-tap Google Pay purchase on the retailer’s site from partners like Wayfair, Chewy, Quince, and select Shopify merchants. For hard-to-find items, an AI-powered “Let Google call” flow uses Duplex plus Gemini to phone local businesses, ask whether they have the product, capture promos, and text you a summary so you don’t sit on hold.

Meanwhile, ChatGPT’s Instant Checkout integration is starting to close the gap on the transaction side: for participating Etsy (and soon Shopify) merchants, you can go from research to ordering inside the chat itself, with Stripe handling payment rails and OpenAI promising not to bias rankings toward merchants that support Instant Checkout. But the core of the new mode is still research—think “personal shopper who hands you a report,” not “agent that stalks price drops in the background.”

Zoom out: What's the impact of this? 

ChatGPT’s search traffic has already crossed 1 billion queries per week, and a huge chunk of that is people asking product questions—quiet vacuums, mid-range gaming laptops, gifts for weirdly specific relatives. On the other side, Google’s Shopping Graph is quietly becoming the default product index for the open web, spanning global brands down to local mom-and-pop inventory.

The strategic split is clear: OpenAI is trying to pull discovery into a conversational assistant that can later hand off to Instant Checkout, while Google is upgrading its existing surfaces—Search, Shopping, Gemini—with richer, AI-driven shopping flows that preserve its search-and-ads DNA.

Behavior is already shifting: AI Mode queries are 2–3x longer than classic “noise cancelling headphones” searches, and people phrase them as full scenarios (“WFH headphones that let me hear the doorbell”) instead of keyword salads. In parallel, OpenAI’s new mode explicitly optimizes for “detail-heavy” categories like electronics, beauty, and home, where buyers obsess over trade-offs and specs before they ever click “buy.”

Why it matters.‍

For everyday shoppers, the win is time: instead of bouncing across 12 tabs, you can ask ChatGPT to “build me a buyer’s guide for three mid-range e-bikes under $1,500” and then use Google AI Mode to sanity-check prices, stock, and local availability—with agentic checkout quietly watching for deals while you sleep.

For builders and brands, the stakes are bigger: Google’s agentic checkout and calling tools sit directly on top of existing Shopping and merchant infrastructure, effectively inserting an AI layer between searchers and sites, while OpenAI’s Instant Checkout inches ChatGPT toward being a commerce front-door that can route orders to Etsy, Shopify, and other marketplaces. If either of these flows becomes “where product discovery happens,” it shifts leverage away from traditional SEO and marketplace search into AI-first funnels that rank items based on model logic, not just bids and blue links.

Counterpoint / uncertainty.‍

A recent BrightEdge study, summarized by the TOI Tech Desk, found that ChatGPT, Google’s AI Overviews, and Google’s AI Mode only agreed on the same brand recommendation in 17% of shopping queries—and gave conflicting brand suggestions in about 62% of them. That’s…not exactly confidence-inspiring if you’re trying to decide which $400 noise-cancelling headphones to buy. Both systems can still hallucinate specs, misstate availability, or overweight certain brands; both are black-box ranking engines layered on top of affiliate-rich, ad-driven ecosystems. And while Google stresses that agentic checkout always asks for permission and uses secure Google Pay, auto-buying on your behalf when a price crosses a threshold is a big behavioral step that regulators and consumer-protection folks are absolutely going to care about.

How to Get the Most Out of ChatGPT’s New Shopping Research Agent (Even on Day One)

The Shopping Research assistant is brand new, which means the internet hasn’t caught up with polished prompt packs yet.

Still, there are already some useful patterns hiding in Reddit threads, blog posts, and OpenAI’s own Shopping Research how‑to. This post pulls those early lessons together and adds concrete prompts you can paste straight into ChatGPT today.

What’s Actually Out There So Far?

Because the feature literally just launched, we’re still in day-zero territory. There’s no widely-shared, polished prompt library for Shopping Research yet, but there are a few useful breadcrumbs:

1. A very honest Reddit thread about shopping with ChatGPT

On r/OpenAI, one user asks: "Can ChatGPT actually help with purchase decisions?" They describe something you may have noticed yourself: sometimes ChatGPT sounds like a must-buy hype-person, and other times it backtracks and tells you the same product is unnecessary.

The gold is in the comments: one reply outlines a structured way to prompt for product research:

• Spell out the use case (who it’s for, environment, how often you’ll use it).
• List hard requirements like budget, region, must-have features, and deal-breakers.
• Define the dimensions you care about (durability, ease of use, total cost of ownership, etc.).
• Then ask ChatGPT to research, compare options, and summarize everything in a table plus recommendations.

Another commenter shares a clever trick: first ask ChatGPT to write a detailed “deep research prompt” for your question, then paste that prompt back in and run it. For them, that two-step produced much more thorough product research.

2. OpenAI’s own guide reads like a prompt spec sheet

OpenAI’s official article, Using shopping research in ChatGPT, isn’t framed as “prompt engineering,” but if you read it that way, a few design assumptions become clear:

• Shopping Research works best on deeper decisions (comparisons, tradeoffs), not quick yes/no questions.
• It’s built to ask clarifying questions about your budget, brands, size, and priorities.
• It surfaces products as it finds them and lets you click Not interested or More like this to steer in real time.
• It culminates in a tailored buyer’s guide: what to consider, top picks, side-by-side comparisons, and links.

In other words, the feature is basically a multi-step, prompt-driven research flow hiding behind a nice UI.

3. Early how-tos and launch coverage

A handful of same-day explainers are already dissecting what Shopping Research can do:

• The Verge walks through how the tool builds you a buyer’s guide and why OpenAI is aiming it squarely at holiday shopping.
• ZDNET puts it to the test as a kind of “mind-reading personal shopper.”
• eesel’s guide to ChatGPT Shopping Research in 2025 leans into practical usage patterns and examples.

None of these are “prompt packs,” but they all reinforce the same idea: Shopping Research wants you to describe your situation conversationally, then refine things together.

Bottom line: there isn’t a definitive “Shopping Research prompt bible” yet. But between Reddit, OpenAI’s docs, and early blog coverage, we can already reverse-engineer a pretty good playbook.

Prompt Patterns You Can Use Right Now

Here are a few concrete patterns you can copy-paste into ChatGPT. They’re based on those early tips, but tuned specifically for the Shopping Research mode.

1. The “Deep Research Prompt” Two-Step

First, ask ChatGPT to design its own in-depth prompt for your situation. Then, run that prompt inside Shopping Research.

Step 1 — generate the meta-prompt:

"I want to use Shopping Research as a deep product researcher.

Write a single, long ‘deep research prompt’ I can paste back into you that will
thoroughly research **[PRODUCT CATEGORY]** for me.

The prompt you write should:
- Ask me clarifying questions about my budget, constraints, and preferences.
- Search the web using Shopping Research and pull from multiple high-quality sources.
- Compare at least 5‑10 options in a table with pros, cons, key specs, and tradeoffs.
- Call out where data might be out of date (price/stock).
- End with a short ‘If I were you…’ recommendation plus 2–3 alternates.

Don’t run the research yet—just output the prompt."


Step 2 — paste the generated prompt back into ChatGPT and run it.

This mirrors the Reddit commenter’s workflow from their purchasing thread. In practice, it forces Shopping Research into a more methodical research pattern instead of a quick, off-the-cuff answer.

2. Requirements + Assessment Dimensions + Clear Steps

This pattern borrows from that same thread but aligns it with how Shopping Research actually works.

"Use Shopping Research to help me pick **[PRODUCT]**.

**Use case:**
– [Describe how you’ll use it: environment, frequency, who it’s for.]

**Hard requirements:**
– Budget: up to $[X]
– Region for availability: [country / city]
– Must-have features: [list]
– Deal-breakers: [list]

**Assessment dimensions (score each 1‑5):**
– Performance
– Reliability / durability
– Total cost of ownership
– Ease of use / setup
– Warranty & support

**Do the following using Shopping Research:**
1. Find 5–10 products that meet my hard requirements.
2. Pull specs, prices, and key pros/cons from high-quality reviews and product pages.
3. Summarize them in a comparison table with the assessment scores.
4. Highlight any hidden gotchas (fees, compatibility issues, battery life, etc.).
5. Recommend 1–2 best options and explain why they fit my use case."


This keeps you in control of what matters, while letting the agent do the heavy lifting on data gathering and synthesis.

3. A Clarifying-Questions-First Prompt

OpenAI’s docs emphasize that Shopping Research will ask follow-ups about your budget, brands, and priorities. You can lean into that instead of waiting passively.

"I want to use Shopping Research for this:

Help me find **[PRODUCT CATEGORY]**.

Before showing any products, ask me 5–10 targeted questions to narrow down:
– Budget range
– Where I live and preferred retailers
– How often I’ll use it and for what
– Any brand preferences or brands to avoid
– Whether I care more about price, performance, longevity, aesthetics, or eco-impact
– Any accessibility, size, or noise constraints

After I answer, run Shopping Research and build a buyer’s guide that:
– Explains the key tradeoffs for someone like me.
– Shows 5–10 concrete options with links.
– Ends with a shortlist of 2‑3 picks I should actually open in tabs."


This matches how Shopping Research is described in both the OpenAI help article and early coverage in places like The Verge.

4. Bake Skepticism Into the Conversation

The original Reddit question was really about trust: how do you avoid “must buy” hype that changes a week later? One answer is to explicitly ask Shopping Research to challenge its own picks.

"After you present your Shopping Research results, I want a second pass where you
argue against your own top picks.

For each recommended product, list scenarios where it would be a bad choice.
Show at least 2 alternative picks that solve those weaknesses.
Flag where your information might be incomplete (pricing, stock, upcoming models).

Keep this ‘devil’s advocate’ lens for the rest of our shopping conversation."


This keeps ChatGPT in the role of research assistant, not oracle. It also nudges the model to surface edge cases and caveats that might otherwise stay buried.

5. A Deals- and Timing-Aware Prompt

Shopping Research launched right in time for Black Friday and holiday sales, and OpenAI has said usage will be “nearly unlimited” during that period. That makes it a natural tool for hunting value, not just products.

"Use Shopping Research to find **[PRODUCT]** where current pricing is meaningfully
below typical street price, especially Black Friday / holiday promotions.

Prioritize value for money rather than absolute rock-bottom price.
Call out any suspiciously low deals that might indicate low-quality brands,
restocking fees, or bad return policies.

Where possible, link to trusted retailers with good return windows and clear
warranty terms."


You still need to double-check prices and policies on the retailer site, but this turns Shopping Research into a pretty capable first-pass filter for seasonal deals.

Putting It All Together

Even on launch day, you don’t have to wait for a fancy “prompt pack” to get real value from ChatGPT’s Shopping Research mode.

• Use Reddit’s “deep research” trick to force more thorough workflows.
• Steal the structure from OpenAI’s own docs: clarifying questions, rich buyer’s guides, and iterative refinement.
• Make your requirements and tradeoffs explicit instead of asking for “best X” in the abstract.
• Bake in skepticism so the agent has to challenge its own recommendations.

From there, you can start building your own internal prompt library — tailored to how you shop, what you obsess over in products, and how much detail you want in a buyer’s guide. Shopping Research gives you the engine; good prompts turn it into a real advantage.

What’s next.

OpenAI’s roadmap hints that today’s buyer’s guides will be wired more tightly into Pulse cards, merchant allowlists, and Instant Checkout so ChatGPT can nudge you toward accessories and follow-on purchases over time. Google’s AI shopping plans point the other way: broader category coverage in AI Mode; tighter Gemini integration in Search and the Gemini app; more merchants onboarded for price-tracking and agentic checkout; and expanded “Let Google call” coverage beyond toys, health/beauty, and electronics.

Short term, that means your Black Friday/Cyber Monday stack is likely “ChatGPT to figure out what you want, Google to make sure you don’t overpay, plus a couple of old-school tabs just in case.” Longer term, both companies are clearly experimenting with full agentic shopping journeys where the AI not only recommends but also executes purchases end-to-end.

‍
If you squint, you can already see the split: ChatGPT is becoming your research analyst—slow, deep, personal—and Google is becoming your power shopper—fast, visual, wired into every merchant and checkout stack. The smartest move for now isn’t picking a winner; it’s learning how to play them off each other so you get the best ideas from one and the best prices from the other.