Deep Research in ChatGPT: How It Works and How to Use It in 2026

Research can quickly become time-consuming when the answer is spread across dozens of websites, reports, PDFs, product pages, and datasets. Finding the information is only one part of the job; you also have to evaluate the sources, compare conflicting claims, extract useful evidence, and organize everything into a report that can actually support a decision.

Deep Research in ChatGPT is designed to make that process easier. Instead of producing a quick response from a single search, it can investigate a complex question across multiple sources, develop a research plan, analyze what it finds, and produce a structured report with citations that you can review.

OpenAI describes Deep Research as a capability for planning, researching, and synthesizing complex questions into documented reports. It can work with the public web, specific websites, uploaded files, and supported connected apps, making it particularly useful when the information you need is scattered across different sources.

For ecommerce teams, marketers, SEO professionals, analysts, and business owners, that creates practical opportunities for competitor analysis, market research, product research, customer-intent analysis, vendor comparisons, content development, and strategic decision-making.

This guide explains how Deep Research in ChatGPT works, how it differs from regular ChatGPT Search, how to write better research prompts, and how businesses can use it effectively without treating AI-generated research as automatically correct.

What Is Deep Research in ChatGPT?

Deep Research is a ChatGPT capability built for questions that require more investigation than a normal conversation or quick web search. Rather than simply retrieving a few results and summarizing them, it is designed to reason across information from multiple sources and turn those findings into a documented research report.

A user first describes the outcome they need and selects the sources ChatGPT can use. ChatGPT can then propose a research plan, which the user can review and modify before the investigation begins. While the research is running, users can follow its progress, interrupt it, change the focus, or adjust the available sources.

The final report contains citations or source links so important findings can be checked against the original evidence. Longer reports can also include a table of contents, a sources-used section, and an activity history showing how the research progressed.

This makes Deep Research particularly useful when the answer to your question cannot reasonably be found on one webpage.

For example, asking "What is agentic commerce?" may only require a straightforward explanation. Asking "How is agentic commerce changing ecommerce in 2026, which major platforms are developing it, what infrastructure is being introduced, and what should Shopify brands prepare for?" requires a much broader investigation.

How Does ChatGPT Deep Research Work?

The exact technical process is more sophisticated than a simple search-and-summary workflow, but from the user's perspective, Deep Research can be understood through five main stages.

1. You Define the Research Goal

The process starts with your prompt. Instead of merely entering a topic, explain what you are trying to understand or what decision the research needs to support.

A broad request such as "Research AI ecommerce" gives very little direction. A better request would specify the platforms, questions, timeframe, audience, and expected outcome.

For example:

Research how ChatGPT, Gemini, and Perplexity are changing ecommerce product discovery in 2026. Focus on product recommendations, shopping capabilities, merchant integrations, AI referral traffic, and implications for Shopify brands. Prioritize first-party sources from OpenAI, Google, Perplexity, and Shopify, then use credible independent research for additional context.

This gives the research process clear boundaries without telling it what conclusion it must reach.

2. You Select the Sources

By default, Deep Research can use the public web and files you upload. It can also use supported connected apps and data services available to your account and permitted by your workspace configuration.

You can also control which websites receive priority. ChatGPT allows you to restrict a research task to selected websites or prioritize specific domains while still allowing broader web research.

That can substantially improve source quality. If you are investigating ChatGPT functionality, for example, OpenAI documentation should generally establish what the product actually supports. Independent publications can then provide analysis, criticism, testing, or market context.

3. ChatGPT Creates a Research Plan

Before the main investigation begins, Deep Research can propose a plan explaining how it intends to approach your question. You can review and modify this plan rather than waiting until the final report to discover that the research went in the wrong direction.

Suppose you request an ecommerce competitor analysis and notice that the proposed plan spends too much time examining social media activity. If your priorities are pricing, product assortment, positioning, customer complaints, and AI visibility, you can adjust the plan before the deeper research starts.

This human-in-the-loop stage is particularly useful for business research because two people researching the same company may require completely different information.

4. Deep Research Investigates the Topic

Deep Research then works through the problem across multiple steps. OpenAI describes the underlying approach as being able to plan and execute a multi-step research trajectory, react to information it encounters, and backtrack when necessary.

That means the investigation does not necessarily follow one rigid sequence. Research into a competitor, for example, might reveal a newly launched product or acquisition that changes the competitive landscape and requires additional investigation.

This iterative process is one of the main reasons Deep Research is more appropriate for complicated questions than a single web search.

5. You Receive a Documented Report

Once the investigation is complete, ChatGPT produces a structured report containing citations or source links. Those citations are important because they allow you to inspect the evidence instead of relying entirely on the generated summary.

Current Deep Research reports can also be downloaded for reuse in formats including Markdown, Word, and PDF. This makes them more practical for teams that want to turn the research into briefs, internal documents, strategy presentations, or further analysis.

Deep Research by the Numbers

OpenAI's original Deep Research launch included several benchmark results that help illustrate the difference between a research-oriented system and a standard model response.

On Humanity's Last Exam, an expert-level benchmark containing more than 3,000 questions across over 100 subjects, the model powering the original Deep Research release achieved 26.6% accuracy. In OpenAI's published comparison, GPT-4o scored 3.3%, Claude 3.5 Sonnet scored 4.3%, Gemini Thinking scored 6.2%, OpenAI o1 scored 9.1%, and o3-mini (high) scored 13.0%.

Deep Research also achieved a 67.36% pass@1 average on GAIA, a benchmark covering real-world questions requiring capabilities such as reasoning, web browsing, multimodal understanding, and tool use. The previous state-of-the-art result shown by OpenAI was 63.64%, while an aggregated Deep Research result reached 72.57%.

These numbers should be interpreted carefully. They are benchmark results associated with the original Deep Research release, not a promise that 26.6%, 67.36%, or any other fixed accuracy level applies to every research report produced today.

The useful takeaway is that multi-step research, browsing, and tool use can materially improve performance on questions where relevant information needs to be found rather than simply recalled.

Deep Research vs ChatGPT Search

ChatGPT Search and Deep Research can both use current information, but they are designed for different kinds of work. OpenAI recommends Search when users need quick facts and Deep Research when a question requires depth, thoroughness, and synthesis across multiple sources.

Task	Better Option
Find a recent announcement	ChatGPT Search
Check a current fact	ChatGPT Search
Find one recent statistic	ChatGPT Search
Explain a straightforward topic	Standard Chat/Search
Compare 10 competitors	Deep Research
Investigate a new market	Deep Research
Analyze multiple reports and PDFs	Deep Research
Build an evidence-backed industry report	Deep Research
Combine internal documents with web research	Deep Research
Evaluate several software vendors	Deep Research

The simplest rule is to consider how much evidence the question requires. If one or two reliable sources can answer it, Deep Research may be unnecessary; if the answer requires investigation across many sources, it becomes much more useful.

How to Use Deep Research in ChatGPT

OpenAI currently provides several ways to start Deep Research. Users can type /Deepresearch in a prompt, select Deep research from the tools menu in the prompt window, or access it through the sidebar where available. Availability and usage limits depend on plan and region.

Before starting the research, spend a few minutes improving the request. A detailed prompt can prevent irrelevant research and make the final report substantially easier to use.

Step 1: Define the Decision

Start by asking what the research will help you decide. Instead of requesting general research about "ecommerce software," explain that your Shopify team is choosing between several AI visibility platforms and needs evidence to decide which one to test.

This shifts the task from information gathering toward decision support. It also helps Deep Research determine which facts deserve more attention.

Step 2: Define the Scope

Set reasonable boundaries around the investigation. This could include a date range, geographic market, customer segment, product category, competitor list, or specific research questions.

For fast-changing subjects such as AI commerce, a date requirement is particularly helpful because capabilities, integrations, pricing, and product availability can change quickly.

Step 3: Specify Source Quality

Tell ChatGPT which types of evidence should be prioritized. For platform features, official documentation is usually the best starting point, while independent research can provide additional testing, criticism, or market context.

You can also ask Deep Research to distinguish between vendor-reported statistics and independently verified evidence. That small instruction can make a business report considerably more trustworthy.

Step 4: Define the Deliverable

Explain how you want the findings organized. A business-focused research report might include an executive summary, key findings, supporting statistics, competitor comparison, risks, opportunities, conflicting evidence, recommendations, and citations.

Giving the research a destination makes the investigation more focused.

Step 5: Review the Plan and Sources

Before the full task begins, inspect the proposed research plan. If the emphasis is wrong, adjust it immediately rather than accepting a report that answers a slightly different question.

While the research runs, you can also monitor progress and interrupt it to refine the focus or change source access.

How to Write a Good Deep Research Prompt

A useful Deep Research prompt generally contains five elements: the topic, objective, scope, source requirements, and desired output.

A reusable template looks like this:

Research [TOPIC] to help me [DECISION OR OUTCOME]. Focus on [QUESTIONS] during [TIME PERIOD]. Prioritize [SOURCES] and clearly distinguish first-party claims from independent evidence. Include relevant statistics and examples, identify conflicting findings or evidence gaps, and organize the final report into an executive summary, detailed findings, comparison table, recommendations, and sources.

The important part is that you are not simply asking ChatGPT to "research everything." You are giving it a research brief similar to what you would give a human analyst.

6 Deep Research Prompts Ecommerce Teams Can Use

Competitor Research Prompt

Research our main ecommerce competitors: [COMPETITORS]. Compare product positioning, pricing, assortment, customer reviews, shipping, returns, content strategy, third-party coverage, and key differentiators. Use first-party sources for factual product claims and independent sources for customer sentiment. Identify weaknesses and opportunities we could realistically exploit.

New Product Research Prompt

Research the market opportunity for [PRODUCT] in [COUNTRY]. Analyze major competitors, typical pricing, common customer complaints, most-valued features, emerging trends, major retailers, and purchase considerations. Prioritize recent evidence and clearly explain limitations where reliable market data is unavailable.

Customer Pain-Point Research Prompt

Analyze public discussions about [PRODUCT CATEGORY] to identify recurring customer complaints, purchase objections, desired features, reasons for switching brands, and language customers use when describing their problems. Separate findings from customer reviews, professional reviews, forums, and other sources.

Content Research Prompt

Research [TOPIC] for an evidence-led ecommerce article. Prioritize original studies, official documentation, recent statistics, industry reports, expert commentary, and customer evidence rather than simply summarizing competing SEO articles. Produce a research brief containing major claims, supporting sources, content opportunities, misconceptions, and areas where first-hand expertise would improve E-E-A-T.

Software Comparison Prompt

Compare [TOOL A], [TOOL B], [TOOL C], and [TOOL D] for an ecommerce team. Evaluate integrations, capabilities, pricing, reporting, ease of implementation, limitations, customer fit, and important differentiators. Clearly distinguish vendor claims from independently supported findings.

AI Shopping Research Prompt

Research how ChatGPT, Gemini, Perplexity, Claude, and Copilot are influencing ecommerce product discovery in 2026. Focus on product recommendations, shopping capabilities, merchant integrations, AI referral traffic, attribution, and agentic-commerce developments. Prioritize first-party platform documentation and identify areas where reliable commercial data is still limited.

These prompts can be adapted rather than copied word for word. The important principle is to define the decision and evidence requirements before asking for the research.

8 Practical Ways Ecommerce Teams Can Use Deep Research

1. Competitor Analysis

Competitor research is one of the clearest applications because the information required is usually distributed across company websites, product pages, reviews, news coverage, marketplaces, and industry publications.

A research task could compare competitors across pricing, product range, positioning, warranties, shipping, customer sentiment, content strategy, differentiators, and market activity. The result gives your team a more complete picture than simply looking at competitor homepages.

2. Product Opportunity Research

Before developing or sourcing a new product, Deep Research can help investigate what customers like and dislike about existing alternatives. Customer reviews, professional testing, forums, retailer feedback, and product specifications can reveal recurring gaps.

If hundreds of customers complain about difficult assembly, limited sizing, poor durability, or a missing feature, those insights may influence product development as well as messaging.

3. Market Expansion Research

Entering a new geographic market involves much more than estimating search volume. Ecommerce businesses may need to understand local competitors, pricing expectations, shipping standards, regulations, marketplaces, consumer preferences, and category trends.

Deep Research can bring those areas into a single initial assessment. Specialist legal, tax, and compliance advice should still be used where consequential decisions depend on those areas.

4. Customer Intent Research

SEO keyword data tells you what people type into search engines, but customer conversations often reveal why those searches happen.

Deep Research can examine reviews, communities, expert sources, and public discussions to identify recurring buying questions, objections, desired features, and comparison criteria. These insights can improve product pages, collection pages, FAQs, advertising, and buying guides.

5. Evidence-Led Content Research

Many SEO articles are created by reading the pages already ranking for a keyword and producing another version of the same information. Deep Research gives content teams an opportunity to go beyond that cycle.

Instead of researching only competitors, ask for primary research, platform documentation, industry reports, original statistics, expert commentary, product specifications, and credible customer evidence. This provides a stronger foundation for original and useful content.

6. Software and Vendor Evaluation

Software comparisons can involve dozens of criteria, making them well suited to structured research. An ecommerce team choosing an AI visibility platform, for example, might need to compare supported AI engines, prompt monitoring, competitor intelligence, product-level visibility, citations, Shopify integration, reporting, pricing, and revenue attribution.

Comergent's guide to the best AI visibility tools for ecommerce brands provides a useful supporting framework for the criteria ecommerce teams should consider when evaluating this category.

7. SEO Competitor Research

Deep Research should not replace dedicated platforms used for keyword databases, backlinks, rank tracking, or technical crawling. It can, however, add a qualitative layer to SEO competitor analysis.

For example, you could investigate why several competing websites have become authoritative sources within a category by examining their editorial expertise, topical coverage, product testing, digital PR, content structure, third-party mentions, and brand positioning. Those findings can then be combined with quantitative SEO data.

8. AI Search Research

AI discovery is evolving quickly enough that businesses increasingly need to research ChatGPT, Gemini, Perplexity, Claude, and other platforms as separate acquisition environments.

Deep Research can help understand how those platforms approach product discovery, what information they use, what new shopping capabilities are being introduced, and where the evidence remains uncertain. If your goal is to measure whether your own brand actually appears for important AI prompts, however, research should be combined with a repeatable monitoring framework such as the one explained in Comergent's AI search visibility tracking guide.

How Deep Research Can Improve E-E-A-T

Deep Research can strengthen E-E-A-T by improving the evidence behind an article, but it cannot create genuine experience that never happened.

Suppose Deep Research compares 20 running shoes using manufacturer specifications, professional reviews, retailer information, and customer feedback. You can accurately explain that your team researched and compared those sources, but you should not claim "we tested all 20 shoes" unless someone actually performed those tests.

The same rule applies to customer research. An analysis of public reviews should not suddenly become "our customer survey" in the final article.

Use Deep Research to improve evidence, source discovery, factual accuracy, comparisons, context, and completeness. Combine that research with genuine expert review, internal data, customer interviews, case studies, product testing, original photography, or other first-hand experience when available.

That combination produces much stronger E-E-A-T than either generic AI content or unsupported claims of experience.

How to Evaluate Sources in a Deep Research Report

Not every citation deserves the same level of trust. A useful way to evaluate a report is to think about sources in tiers.

Primary sources include official documentation, government data, original studies, regulatory publications, company filings, and original datasets. These are generally preferable for establishing what a platform supports, what a law says, or what a study actually found.

High-quality independent sources include respected journalism, professional testing organizations, credible industry research, and specialist publications. These sources can provide valuable external analysis that a company's own documentation cannot.

Expert and community evidence includes professional blogs, forums, Reddit discussions, customer reviews, and specialist communities. These can be particularly useful for understanding customer experiences, sentiment, problems, and real-world use cases.

The best source therefore depends on the claim. Product pricing should generally come from the company or retailer, while customer dissatisfaction may be better understood through genuine customer feedback.

Can Deep Research Analyze Uploaded Files?

Yes. Uploaded files can be included as sources within a Deep Research task, allowing ChatGPT to combine your existing material with information gathered from the web.

This can be particularly useful if you already have competitor reports, customer surveys, product specifications, internal research, sales documents, PDFs, or strategy material. Instead of starting from zero, the research can build on information your organization has already collected.

For example, you might upload a competitor analysis created in 2025 and ask Deep Research to verify what has changed in 2026, identify new competitors, update pricing, and flag outdated conclusions.

Can Deep Research Search Specific Websites?

Yes. Users can specify websites and either restrict research to those domains or prioritize them while still allowing broader web search.

This is useful when primary evidence matters. Research about OpenAI can prioritize OpenAI documentation, while Gemini research can prioritize Google and Shopify platform research can prioritize Shopify.

Independent sources can then be used for additional context rather than being relied upon to establish basic platform facts.

Can Deep Research Use Connected Apps?

Deep Research can use supported connected apps and data services that are available to the user's account. OpenAI's current documentation gives examples including document stores such as Google Drive and SharePoint as well as authenticated data services such as FactSet and PitchBook.

This allows some research workflows to combine external web information with internal company information. Access remains dependent on permissions, account configuration, and the services connected to ChatGPT.

How to Verify a Deep Research Report

A cited research report should still be reviewed before it becomes the basis for a major business decision or published claim. OpenAI itself advises users to verify important information because ChatGPT can still make mistakes, misinterpret information, lack access to relevant sources, or express incorrect conclusions confidently.

Start by checking whether important claims are actually supported by the cited source. Then check publication dates, sample sizes, methodology, geographic limitations, conflicts of interest, and whether the source is reporting first-hand data or repeating another publication.

Pay particular attention to vendor statistics. A company saying that customers achieve a particular result is different from independent evidence showing the same result across the wider market.

Good research should also acknowledge uncertainty. If several credible sources disagree, the report should explain the disagreement rather than quietly selecting whichever number best supports the desired narrative.

Common Deep Research Mistakes to Avoid

One common mistake is using Deep Research for questions that require only a quick lookup. A full multi-source investigation adds unnecessary complexity when a straightforward current fact can be answered with Search.

Another problem is providing an extremely broad prompt. Requests such as "research ecommerce" or "research AI" leave too many decisions undefined and often produce a report that is comprehensive without being useful.

Source quality is equally important. If you need authoritative information, specify which primary sources should be prioritized and ask the research to separate first-party claims from independent evidence.

Avoid asking the system to prove a conclusion you have already chosen. A prompt such as "find evidence showing why our product is better than Competitor X" encourages confirmation bias. Asking it to compare the evidence supporting and challenging each product produces more useful research.

Finally, do not publish a Deep Research report unchanged. Verify important claims, improve the structure, add genuine expertise and examples, remove irrelevant material, and adapt the research to your audience.

Deep Research vs AI Visibility Monitoring

Deep Research and AI visibility monitoring solve two related but different problems. Understanding the difference is especially important for ecommerce brands.

Deep Research can answer a question such as "Which running-shoe brands are frequently recommended for marathon beginners, and what evidence supports those recommendations?" It can investigate the market, brands, product characteristics, expert reviews, and customer discussions.

AI visibility monitoring asks whether your brand actually appears when customers ask ChatGPT, Perplexity, Claude, Gemini, and Copilot those questions repeatedly over time.

A single Deep Research report is therefore not the same as continuous prompt monitoring.

If your goal is improving ChatGPT product discovery specifically, Comergent's guide on how to rank on ChatGPT for ecommerce explains the optimization side of the process.

How Comergent Fits Into the Research-to-Visibility Workflow

For Shopify brands, Deep Research can form the first stage of a broader AI-commerce workflow. It can help teams understand the market, identify competitors, analyze customer needs, investigate category trends, and determine what information influences purchase decisions.

The next step is measuring what AI assistants actually show customers. That means monitoring relevant prompts across ChatGPT, Perplexity, Claude, Gemini, and Copilot to understand where your brand appears, which competitors are being surfaced instead, and which parts of your product information or content need improvement.

Comergent measures this in Shopify orders, not screenshots of a single prompt. Prompt Watch tracks the buyer questions where a competitor gets recommended instead of you, and Revenue Attribution tags every order with the assistant that sent it, so a sale arrives marked "Referred by: ChatGPT." Visitors referred this way convert at 4x the rate of organic search visitors.

Research can tell you why a competitor may be strong. Monitoring shows you whether that competitor is the one ChatGPT recommends when a buyer is ready to purchase.

The next phase is already arriving: AI agents that shop on the buyer's behalf and complete the purchase with no human click. Comergent's Agentic Feed structures your catalog so those agents can find and evaluate your products, and MCP, ACP, and UCP protocol support lets them buy from your store directly.

Final Thoughts

Deep Research in ChatGPT is most useful when your question cannot be answered properly by opening one webpage or running one search. It provides a structured way to define an objective, select sources, review a research plan, investigate information across multiple steps, and receive a documented report that can be checked through citations.

The benchmark results published with the original Deep Research release also demonstrate why this approach can be valuable for difficult information-gathering tasks. The system achieved 26.6% on Humanity's Last Exam and 67.36% pass@1 on GAIA, although those figures should be treated as historical benchmark results rather than guarantees of accuracy on individual research tasks.

For businesses, the strongest applications are practical rather than experimental. Use Deep Research when you need to compare competitors, investigate a market, understand customer problems, evaluate software, analyze a product category, develop evidence-led content, or bring together information scattered across many sources.

The quality of the final report still depends heavily on the research brief. Define the decision you are trying to make, set reasonable boundaries, prioritize reliable sources, ask for contradictory evidence, and verify consequential claims before publishing or acting on them.

For ecommerce brands, the next step is connecting research with actual market visibility. Deep Research can help explain what customers care about and why competitors may have an advantage, while AI visibility monitoring can show whether ChatGPT, Perplexity, Claude, Gemini, and Copilot are actually recommending those competitors when shoppers ask what to buy.

Used together, research, AI visibility data, product information, and ecommerce analytics can turn AI from a general research tool into a practical source of business intelligence.