Verified against ChatGPT · 2026-07-29
Give Deep Research a mission brief precise enough to trust the citations
Structures a Deep Research task with an explicit scope boundary, source-quality bar, and deliverable shape, so the agent spends its research budget on the actual open question instead of drifting into adjacent, easier-to-answer territory.
The prompt
Ready to copy — highlighted parts are example details you can swap.
You are about to run as ChatGPT's Deep Research agent on the task below. Before browsing, treat this brief as the scope contract for the whole research run — every source pulled and every claim made should trace back to answering the core question, not an adjacent one that happened to have easier-to-find sources. CORE QUESTION Which of the three enterprise SSO providers has the most reliable SCIM provisioning track record for a company our size, based on documented outages and integration complaints, not marketing claims? WHAT WOULD MAKE THIS RESEARCH USELESS A report that just restates each vendor's own marketing page in different words, with no independent evidence of actual reliability. SOURCE QUALITY BAR Vendor status-page incident histories, independent review-site comments mentioning SCIM specifically, and engineering blog posts describing a real migration — not vendor blog posts or affiliate comparison sites. TIME WINDOW FOR RELEVANCE Last 18 months only — SSO reliability changed significantly after each vendor's 2025 platform rewrites. DELIVERABLE SHAPE A comparison table (provider, documented incidents, SCIM-specific complaints, source) followed by a three-paragraph recommendation, not a long narrative essay. RESEARCH DISCIPLINE Stay anchored to the core question even when a more easily-answered adjacent question surfaces during browsing — a research run that quietly substitutes a nearby, better-documented question for the actual one is a specific and common failure, and it produces a report that reads as thorough while not actually answering what was asked. Apply the stated source-quality bar as a hard filter, not a preference — if primary sources or the required source type genuinely don't exist for part of the question, say that explicitly as a finding in itself rather than quietly substituting a lower-quality source and presenting it with the same confidence as a source that met the bar. Respect the time window for relevance strictly — a source that was accurate before the stated window but has since been superseded is worse than no source at all if presented as current, since it creates false confidence rather than an honest gap. Where two sources disagree on a material fact, present both positions and their sources rather than silently picking the one that fits a cleaner narrative — the disagreement itself is often the most useful thing to know. Distinguish clearly between what a source states directly and what is being inferred or synthesized across sources — a reader needs to know which sentences they could go argue with a single source about and which represent connecting of separate facts. If the disqualifying-outcome condition described above is what's being headed toward, stop and say so explicitly rather than completing a full report that hits the disqualifying condition anyway. OUTPUT FORMAT Deliver the report in the shape specified above. At the top, include a scope note: what the core question actually resolved to being about after research, and any sub-question deliberately not chased because it was outside scope even though it came up. Cite every material claim inline. Close with an explicit list of what remains genuinely unresolved or where sources disagreed, rather than smoothing every finding into a single confident narrative.
Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
Deep Research runs as an autonomous multi-step browsing agent that decides its own next search query based on what it's already found, which means an underspecified core question genuinely can drift — the agent's own search results progressively reshape what it treats as "the question," and if that drift isn't explicitly guarded against, the final report can be internally coherent and well-cited while quietly answering a narrower or easier version of what was actually asked. A hard source-quality bar matters specifically because the agent's search results routinely surface vendor marketing content and SEO-optimized comparison sites ranked highly for exactly this kind of comparison query, and without an explicit instruction to name a real gap rather than fill it with a lower-quality source, the report can present a marketing claim with the same citation-backed confidence as an independently verified fact — a materially more dangerous kind of wrong than an honest "no good source exists for this." Requiring disagreeing sources to be shown rather than resolved into one narrative addresses how these reports get used downstream — a single confident number extracted from a report and repeated as fact in a decision meeting is far more consequential than the report itself, and if the underlying sources actually disagreed, that disagreement is exactly the information a decision-maker needs and the one thing a smoothed narrative erases. The explicit instruction to stop and flag a disqualifying outcome rather than complete the report anyway matters because Deep Research otherwise always produces a polished-looking deliverable regardless of whether the underlying research actually succeeded — a report's professional formatting carries no signal about whether it answered the question, so that judgment has to be made explicitly rather than inferred from the fact that a report exists at all.
Verified against
ChatGPT GPT-5.1 (Deep Research) · 2026-07-29
Changelog
- 2026-07-29 — Initial publish, verified against ChatGPT GPT-5.1 Deep Research.
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