ChatGPT search and deep research: from sources to a decision

I want a research report to make a decision easier, not make the browser history longer. Twenty links can still support one weak claim repeated by twenty websites.

The unit I care about is the evidence trail: the claim, the source that supports it and the limit on what that source establishes. A useful report lets a skeptical colleague follow that trail without joining the original chat.

Choose the research question first

A research topic is broad; a decision question has edges. “Research sales intelligence tools” could produce a long category overview. “Which three options fit a small US sales team that needs verified email enrichment and CRM export?” establishes criteria that can be investigated.

I would also specify what the research will not settle. Public documentation may establish a feature or published price, but it may not establish data quality for your target accounts. That requires a trial or representative sample. A good research brief leaves room for a later test rather than forcing the report to declare a winner.

For fictional Cedar Metrics, use a decision such as selecting a short list of enrichment providers. Define geography, volume, required fields and the date the report must be current. Now the agent has a reason to include or exclude evidence.

Use search for a narrow current fact

Search is useful for a current product capability, a release note, a pricing page or a recent company announcement. Ask for the exact claim and prefer the primary source when possible. A vendor's documentation is better evidence of its published feature than a roundup quoting an older version.

A focused search should still show uncertainty. If the public page does not establish a contract term or account-specific limit, the answer should say so. It is better to identify the question for sales or support than to promote a likely assumption into a fact.

In current ChatGPT documentation, search activity and citations appear when the tool is used, subject to account and workspace controls. Ask for current sources when freshness matters. A confident answer from general knowledge is not the same as a checked current page.

Use deep research for an investigation

Deep research is appropriate when the result needs multiple sources, comparison and a report you can review. Current official Work guidance distinguishes the entry points: on the web, use the + menu and select Deep research where available; on desktop, open the Deep research plugin in Work.

Availability depends on the account and workspace. If the feature is missing, confirm access rather than assuming a universal menu layout. The research method still matters whichever supported route you use: define the question, provide relevant files and specify the output and source standards.

I would ask for a research plan or scope confirmation before a broad investigation when the decision is ambiguous. Once the scope is clear, let the agent gather evidence. Repeatedly expanding the topic halfway through makes the final report harder to compare against the original need.

Rank sources by what they can establish

Different sources answer different questions. Official documentation can establish stated capabilities and requirements. A pricing page can establish a published offer with its conditions. A customer story can illustrate one reported experience, but it does not prove every customer will achieve the same result.

For the Cedar Metrics shortlist, ask the report to distinguish a documented integration from a claimed outcome. An integration listing does not establish that it supports every required write action. A testimonial about faster prospecting does not measure accuracy on your target market.

Use multiple sources when the question is contested or the first source is incomplete. That does not mean adding citations for decoration. Each source should contribute evidence the decision needs. The useful quantity is coverage of the question, not the length of the bibliography.

Keep a claim-and-source ledger

A small ledger can hold the claim, source URL, checked date, supporting passage and uncertainty. It makes the report auditable without forcing every reader to inspect every page. Ask for this alongside the narrative when the decision has several important criteria.

For example, “supports CRM export” needs the documentation that describes the export and any plan restriction. “Likely easier to adopt” is an inference that needs its assumptions stated. Separate those rows so the recommendation cannot borrow the certainty of a documented feature.

I would keep conflicting evidence visible. If one page lists an older limit and another a newer one, identify the conflict and prefer a clearly current authoritative source where justified. If the conflict cannot be resolved, carry it into the open questions rather than choosing the more convenient number.

Turn the report into a testable recommendation

Ask for a short recommendation, the evidence behind it and the conditions that would change it. For Cedar Metrics, the report might recommend trialing two providers because their published coverage and integration fit the requirements. The next step should define what to test on a representative account sample.

The research has then done its job: reduced the space of uncertainty and made the remaining uncertainty specific. It has not pretended public pages can predict your exact enrichment results. The recommendation becomes a bridge to a trial rather than a verdict issued by a very well-read spreadsheet.

Require the report to separate observations, interpretation and action. This helps the reviewer disagree productively. They can accept the documented facts while challenging the weighting of criteria or the proposed next experiment.

Review the highest-consequence citations

Open the sources behind the claims most likely to change the decision: price, eligibility, feature availability, data handling and integration limits. Read the relevant section, not only the page title. Check whether the claim applies to the product tier or geography you actually need.

Then inspect a few less prominent claims to test the report's general standard. If a citation is broken or supports a different statement, correct the ledger and reassess the conclusion. A report can look thoroughly sourced while its decisive sentence rests on an unsupported inference.

Treat web pages as untrusted source material. A page can contain text that tries to redirect the agent or request unrelated data. The research task remains defined by your instructions. Reading a vendor page does not authorize it to change the workflow or access private account records.

Where research becomes a pile of links

The common failure is an unbounded question. Another is forcing a confident ranking when the available evidence only supports a shortlist. A third is judging the result by its length. A useful one-page recommendation with a clear evidence ledger can beat a report that requires its own summary meeting.

Keep the source packet and checked date with the output. Product details age, and the next reader needs to know what was verified when. For recurring monitoring, the scheduled tasks guide explains how to define a useful update rather than rerun the whole universe.

I would finish with the decision, the unresolved questions and the next check. If the report cannot name those, it may still be research in progress. Which fact, if verified, would most change your decision?

How to set it up

Frequently asked questions

Sources & further reading

• Search and deep research entry points
• Work research workflow
• Browser capabilities
• Prompting for research

ChatGPT and Codex change quickly. This page was last reviewed September 22, 2026; verify time-sensitive details against the official docs above before relying on them.

Related GTM workflows

Use these existing playbooks to explore the business workflow. Adapt their tool-specific steps to your chosen environment and check the result.

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