ChatGPT’s new “shopping research” feature turns the chatbot into an interactive product researcher that builds personalized buyer’s guides using live data from across the web. It aims to replace endless tabs and comparison blogs with a single, conversational experience that helps users decide what to buy faster and with more confidence.
What is shopping research in ChatGPT?
Shopping research is a new in-chat experience that helps users find, compare, and evaluate products directly inside ChatGPT. Instead of giving a short list of generic recommendations, it gathers product details like price, availability, specs, images, and reviews to build a structured buyer’s guide tailored to the user’s needs.
The feature is available to logged-in users on Free, Go, Plus, and Pro plans, on both web and mobile, with nearly unlimited usage during the holiday shopping period. This positions ChatGPT as a serious alternative to traditional search engines and e-commerce discovery experiences, especially for high-consideration purchases such as electronics, appliances, and home goods.
How shopping research works
The experience starts when a user asks a shopping-focused question such as “best laptops under ₹70,000 for coding” or “quiet cordless vacuum for a small apartment.” ChatGPT then asks smart clarifying questions about budget, use case, brand preferences, and must‑have features before it shows any products, which helps it collect detailed requirements up front.
In the background, a shopping-specialized version of GPT‑5 mini searches trusted sites to gather up‑to‑date information, then synthesizes everything into a coherent buyer’s guide. Users can refine results by marking products as “Not interested” or “More like this,” and the system updates recommendations in real time based on that feedback.
Key features and capabilities
Shopping research focuses on the most time‑consuming part of the buying journey: deciding what to buy, not just where to buy it. It is designed for deeper, multi‑constraint queries that involve trade‑offs, like balancing performance, price, and brand reputation in a single decision.
Core capabilities include:
- Collecting user preferences via clarifying questions before recommendations.
- Aggregating specs, pricing, availability, images, and review signals from multiple reputable retailers.
- Structuring output as a human‑readable buyer’s guide with categories, explanations, and pros/cons.
- Adapting interactively when the user rejects or prioritizes certain products.
OpenAI’s internal evaluations indicate that this shopping-specialized model achieves higher “product accuracy” on complex, multi‑constraint queries than previous search‑style responses in ChatGPT, meaning its recommendations better match user requirements like price, material, and technical specs.
Where shopping research works best
OpenAI highlights categories where people typically perform heavy research: electronics, beauty, home and garden, kitchen and appliances, and sports and outdoor gear. These categories tend to have many similar products, complex specifications, and strong dependence on current pricing and availability, which benefit from AI‑driven aggregation.
The timing of the rollout—just ahead of Black Friday and the holiday shopping season—shows that OpenAI sees high-intent gift shopping as a prime use case. By offering the feature broadly and with generous usage limits during this period, OpenAI aims to make ChatGPT a default destination for users researching gifts and big-ticket purchases.
Why this matters for search and e‑commerce
Shopping research signals a shift from keyword search to intent‑driven conversations for product discovery. Instead of users manually adapting their queries to fit search engines, the AI collects and structures long‑tail requirements automatically, then translates them into a curated set of options.
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