Verified against ChatGPT · 2026-07-22
Brief Deep Research properly so a ten-minute run is not wasted
A structured brief for ChatGPT Deep Research that fixes scope, source quality, and output format upfront, since a Deep Research run cannot be redirected once it is already underway.
The prompt
Ready to copy — highlighted parts are example details you can swap.
Run this as a Deep Research task, not a quick answer. RESEARCH QUESTION Which of these three payroll platforms handles multi-state contractor tax filing most reliably? DECISION THIS SUPPORTS Choosing a payroll vendor to switch to before next quarter, has to support contractors in 6 states SCOPE - Time window: Last 18 months, this space changes with tax law updates - Geography or market: United States only - Explicitly out of scope: International payroll, enterprise-tier pricing above 500 employees SOURCE BAR - Prioritize primary sources, official filings, original studies, direct vendor documentation, over secondary summaries or aggregator blog posts. - When sources disagree, say so explicitly and name both positions rather than picking one silently. - Flag any claim that rests on a single source with no corroboration. OUTPUT FORMAT 1. A three-sentence bottom line answering the research question directly. 2. A comparison table if more than one option is being evaluated: Gusto, Rippling, and Deel. 3. Key findings grouped by sub-topic, each with an inline citation. 4. What you could not confirm, and why. Do not fill a gap with a plausible-sounding guess. 5. A short section on what would change the answer, so I know how fragile the conclusion is. If the question as stated is too broad to research well in one pass, say so and propose a narrower version before running. If you are on GPT-5.1 and can set a reasoning effort level for this chat, set it to high before running this brief. Multi-step research benefits from more thinking time than the default balanced setting.
Customize the highlighted detailsoptional — the prompt above already works
Why this works
Deep Research runs as an autonomous multi-step agent that browses and reads dozens of sources over several minutes before producing a cited report, and unlike a normal chat turn, you cannot course-correct it two minutes into a run the way you would nudge a reply that is going in the wrong direction, so the entire value of the output depends on how precisely the brief is written before it starts. Naming the decision this research supports gives the agent a relevance filter, what actually counts as a key finding versus noise, instead of leaving it to try to be exhaustive about the raw topic, which is how a research run comes back thorough but unusable. Stating a source-quality bar matters because an unconstrained agent otherwise weights a well-optimized blog post and a primary filing similarly at the retrieval stage; naming primary sources as the priority changes what it chooses to read, not just what it chooses to cite. The mandatory what you could not confirm section counters a specific tendency of long research summaries to smooth over a scarcity of good sources on one sub-question rather than flag it, because an honestly incomplete answer is more useful than a comprehensive-sounding one that quietly papers over a gap.
What you get back
Bottom line: Gusto and Rippling both handle multi-state contractor filing reliably; Deel is strong internationally but has had recurring user-reported delays with US 1099 filings in two states this year. Comparison table: [Gusto | Rippling | Deel] x [multi-state support | pricing tier | reported issues]. Could not confirm: exact 1099 filing SLA for Rippling in Texas specifically, vendor documentation does not state it and no independent source addressed it directly.
Verified against
ChatGPT Deep Research (GPT-5.1) · 2026-07-22
Changelog
- 2026-07-22 — Initial publish, verified against ChatGPT Deep Research on GPT-5.1.
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