A polished paragraph is not evidence. A citation is not automatically a good source. And five websites repeating the same claim may still trace back to one weak original. Treat ChatGPT as a research copilot that helps you find, organize, compare and challenge evidence — not as the evidence itself.
Use the right mode for the job
OpenAI describes Deep Research as a tool for complex questions that can use public web sources, uploaded files and enabled apps, with citations or source links in the resulting report. OpenAI: Deep Research in ChatGPT. ChatGPT Search is intended for current web information with source links. OpenAI: Search.
1Turn the topic into a decision-grade question
“Research AI agents” is not a useful research brief. Define the decision, population, geography, time period and constraints.
| Weak question | Decision-grade version |
|---|---|
| Is AI good for sales? | Which parts of inbound lead qualification can a 50-person B2B company automate with AI in 2026 without creating unacceptable compliance or customer-experience risk? |
| Which CRM is best? | For a 20-person real-estate sales team using WhatsApp and inbound web leads, compare three CRMs on integration, automation, reporting, total cost and migration risk. |
| Should I enter this market? | What evidence would support or reject entering [market] with [offer] for [customer], assuming a maximum CAC of [X] and a 12-month target of [Y]? |
Turn my topic into a research brief before doing the research. Topic: [TOPIC] Decision I may need to make: [DECISION] Audience for the final report: [ME / EXECUTIVE TEAM / CLIENT / INVESTORS] Geography: [WHERE] Time period: [CURRENT / LAST 12 MONTHS / HISTORICAL RANGE] Constraints: [BUDGET / COMPANY SIZE / REGULATION / TECH STACK / ETC.] Return: 1. The primary research question 2. 5–8 sub-questions that must be answered 3. What evidence would change the decision 4. What terms need precise definitions 5. What information is likely to be time-sensitive 6. What would remain uncertain even after good research Do not start the full research yet.
2Set a source hierarchy before searching
Tell ChatGPT which sources deserve more weight. The hierarchy depends on the topic, but a useful default is:
- Primary sources: laws, regulators, company filings, official product documentation, original research papers, official datasets.
- High-quality secondary sources: reputable research institutions, established journalism, respected industry analysis.
- Practitioner evidence: case studies, expert interviews, technical write-ups — useful but often contextual.
- Community evidence: Reddit, forums, reviews and social posts — valuable for discovering pain points and real experiences, but not strong proof of broad facts.
Use this source policy for the research: - Prefer primary sources for factual claims whenever available. - Use high-quality independent secondary sources to add context or challenge primary-source claims. - Use vendor content for facts about the vendor's own product, but not as independent proof that the product is superior. - Use community discussions only for experiences, complaints, edge cases and hypotheses — not as proof of market-wide facts. - For every material claim, record the source, publication/update date and source type. - If multiple articles repeat a claim but point back to the same original source, treat that as one evidence chain, not multiple independent confirmations.
3Force an evidence table, not just prose
Before asking for a beautiful report, ask for the skeleton. This makes weak claims much easier to spot.
Build an evidence table before writing the conclusion. Columns: - Claim - Evidence summary - Source - Source type: primary / independent secondary / vendor / community - Publication or update date - Directly supports claim? yes / partly / no - Confidence: high / medium / low - What would falsify or weaken the claim Do not hide disagreements between sources. If evidence is missing, write “not established” instead of filling the gap with inference.
4Ask for contradictions on purpose
If the model only searches for evidence supporting your initial idea, you can get a beautifully researched confirmation bias machine.
Now try to disprove the emerging conclusion. Search specifically for: - credible sources that disagree - newer evidence that supersedes older evidence - different populations or markets where the result changes - definitions that make apparently conflicting claims both technically true - commercial incentives or methodological limitations that could bias a source Return the strongest counter-evidence first. Do not create artificial “balance” when one side has substantially stronger evidence; describe the evidence asymmetry clearly.
5Make freshness visible
A correct 2023 fact can be wrong for a 2026 decision. For changing topics — AI tools, prices, laws, leadership, product features, schedules, market conditions — freshness is part of evidence quality.
- Ask for publication and update dates.
- Separate event date from article publication date.
- Prefer current official documentation for current product behavior.
- State when the newest reliable evidence is older than the decision requires.
- Do not silently combine different time periods as if they described one current state.
6Spot-check the citations yourself
For an important report, open a sample of the citations. Do not only check whether the link exists — check whether it supports the exact sentence.
Audit the research as if you are a skeptical editor. Select the 10 claims that matter most to the conclusion. For each claim: 1. Show the exact conclusion being supported 2. Identify the strongest source 3. Explain why that source is appropriate 4. State whether the source directly supports the claim or only supports an inference 5. Flag any claim that is stronger than the evidence 6. Suggest safer wording where needed Then list the 3 claims I should manually verify before acting.
7Turn research into a decision memo
The last step is not “summarize everything.” It is to show what the evidence means for the decision.
Turn the verified research into a decision memo. Structure: 1. Executive answer in 5 sentences maximum 2. What we know with high confidence 3. What we think is likely, but evidence is incomplete 4. What remains unknown 5. Strongest evidence against the preferred option 6. Decision options with trade-offs 7. Recommended next validation step — the cheapest action that would reduce the most important uncertainty 8. Source list Do not invent a recommendation if the evidence is genuinely insufficient. Say “insufficient evidence” and specify what would resolve it.
The full master prompt
Act as a rigorous research analyst, not a persuasive writer. Research question: [QUESTION] Decision context: [WHAT DECISION THIS RESEARCH WILL INFORM] Scope: - Geography: [X] - Time period: [X] - Population / customer / company type: [X] - Constraints: [X] Research protocol: 1. Clarify ambiguous terms before research. 2. Break the question into sub-questions. 3. Prefer primary and authoritative current sources. 4. Build an evidence table with claim, source, date, source type and confidence. 5. Search deliberately for contradictory evidence. 6. Separate facts, source claims, estimates and your own inferences. 7. Flag outdated evidence and population mismatches. 8. If sources disagree, explain why and which evidence is stronger — do not average the disagreement away. 9. Do not use the existence of a citation as proof that the citation supports the sentence. 10. Mark unsupported claims as “not established.” Final output: - executive answer - evidence table - strongest supporting evidence - strongest counter-evidence - uncertainties and missing data - decision options and trade-offs - 3 manual checks I should perform - sources with dates Important: confidence should reflect evidence quality, not how confident the prose sounds.
Red flags that should make you stop
| Red flag | Why it matters |
|---|---|
| No source for a precise number | Specificity can create false trust. Find the original dataset or remove the precision. |
| Source date is missing | You cannot judge whether it describes the current state. |
| Vendor says its own product is “best” | Useful product information, weak independent comparative evidence. |
| Ten pages repeat the same statistic | They may all copy one unverified original. |
| Evidence from another country or population | It may be informative but not directly transferable. |
| Conclusion is stronger than the source | Common failure mode: source says “associated with”; summary says “causes.” |
OpenAI's current Deep Research experience provides citations or source links and is designed for multi-step investigation, but a cited report can still include weak sources, outdated evidence or interpretation errors. For high-stakes legal, medical, financial or safety decisions, use qualified professional advice and authoritative primary sources rather than relying on an AI report alone.
My 60-second final check
- Can I state the exact research question in one sentence?
- Did I define geography, population and time period?
- Are the most important claims supported by strong sources?
- Did I actively search for counter-evidence?
- Did I check whether dates are current enough?
- Did I manually open the sources behind the claims that drive the decision?
- Are fact, estimate, source opinion and inference visibly separated?
- Do I know what remains uncertain?
The quality of the question, evidence hierarchy and verification process matters more than a clever prompt.
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