Deep Research

Deep Research Guide

Produce a trustworthy synthesized answer, not a pile of links. The output of research is a decision-grade brief: a clear claim, the evidence behind it, citations, and an honest map of what is still unknown. Speed without verification is a liability — a confident wrong answer costs more than a slow right one.

Core principle: default to skeptical. Treat every key claim as guilty until corroborated. Your job is not to confirm a hypothesis — it's to find what would break it.

Workflow — plan → gather → verify → synthesize

Run these as distinct phases. Don't start writing the report while you're still gathering, and don't gather before the question is scoped.

1. Plan — scope the question

Before searching, pin down what's actually being asked. Most bad research answers a question nobody asked.

• State the question in one sentence. If you can't, it's not scoped yet.
• Name the decision it serves. "Which X should we pick?" needs different evidence than "Is X true?". The decision sets the bar for confidence.
• List the answer's shape. A number? A recommendation? A comparison table? A yes/no with caveats? Knowing the shape tells you when you're done.
• Set boundaries: time window (recency that matters), geography, scale, definitions of fuzzy terms.

Narrow an underspecified question before spending effort. If the ask is "what should I buy / which tool / is this a good idea" without budget, use-case, constraints, or context, ask 2–3 clarifying questions first. Researching the wrong question thoroughly is still wrong.

Then decompose into sub-questions — the 3–7 things that must each be answered for the whole to hold. Research the sub-questions; assemble the answer.

2. Gather — fan out across sources

• Cast wide before going deep. Run several differently-worded queries; don't anchor on the first source's framing. Search for the counter-claim too ("X is overrated", "problems with X") — not just confirmation.
• Go to primary sources. Prefer the original study, filing, spec, dataset, or official doc over an article summarizing it. Summaries drift; numbers get garbled in retelling.
• Triangulate. A claim is only as strong as the number of independent sources that confirm it. Three outlets all citing one press release is one source, not three.
• Capture as you go: for each fact, note the source, the date, and a direct quote/figure. You cannot cite what you didn't record.

3. Verify — adversarial check (the step people skip)

For each key claim (the ones the conclusion rests on), actively try to refute it:

• Find the origin. Trace the claim to its source. Where did this number actually come from? Who measured it, how, and when?
• Look for the strongest disagreement. Who says the opposite, and why? A claim you can't find any dissent on is either settled or you haven't looked hard enough.
• Check the math and the units. Percentages without a base, totals that don't add up, growth rates with no time frame, and apples-to-oranges comparisons are the common tells.
• Test recency. Is this still true? Prices, rankings, "fastest/largest/only" claims, and policy facts decay. A correct 2019 fact can be a wrong 2026 answer.
• Watch for self-interest. Vendor benchmarks, sponsored studies, and anything selling something get an extra round of scrutiny.

If a claim survives a genuine attempt to break it, it's load-bearing. If it doesn't, demote it to "reported but unverified" or drop it.

4. Synthesize — write the brief

• Lead with the answer. First line: the conclusion / recommendation. Decision-makers read top-down and may stop after the first paragraph — make it count.
• Then the why, structured by sub-question, each point carrying its citation.
• Separate fact from inference explicitly (see below).
• Close with confidence + unknowns.

Source-credibility checklist

Score each source before you lean on it:

• Primary or secondary? Original data/document > reporting on it > commentary on the reporting.
• Authority — does the author/org actually have standing on this topic, or are they out of their lane?
• Recency — is it current enough for a claim that changes over time? Note the date, always.
• Independence — funded by, owned by, or selling the thing in question? Conflicts bias.
• Method transparency — can you see how they got the number (sample, methodology, sources), or are you trusting an assertion?
• Corroboration — do independent sources agree? Outliers need explaining, not silent dropping.
• Track record — has this source been reliable/retracted before?

Rough hierarchy (context-dependent, not absolute): peer-reviewed studies, official filings/standards, and primary datasets at the top; reputable journalism and expert analysis in the middle; anonymous posts, marketing, and AI-generated content summaries near the bottom. A low-tier source can still be right — it just needs corroboration before it carries weight.

Separating fact from inference

Be ruthless about which is which; conflating them is how research misleads.

• Fact — directly stated by a credible source, with a citation. ("Revenue was $4.2M in 2025 [source].")
• Inference — your reasoning from facts. Label it. ("This implies ~30% YoY growth, assuming the 2024 figure of $3.2M is comparable.")
• Assumption — something you're taking as given without evidence. Name it so the reader can challenge it. ("Assuming the same accounting basis across years.")
• Unknown — a gap you couldn't fill. State it; don't paper over it.

Phrases that signal you're doing it right: "according to…", "this suggests…", "I could not find…", "sources disagree on…".

Reporting confidence and unknowns

End every brief with an explicit confidence statement. Vague hedging ("seems like") is useless; calibrated confidence is actionable.

• High — multiple independent primary sources agree; recent; verified the underlying math.
• Medium — corroborated but with gaps, dated data, or some reliance on secondary sources.
• Low — single source, conflicting evidence, stale data, or heavy inference. Say so loudly.

Always include a short "What I couldn't verify / what would change this answer" section. Naming the unknowns is a feature: it tells the decision-maker where the risk lives and what to check before betting on it.

Report template

ANSWER:        <the conclusion / recommendation, one or two sentences>
CONFIDENCE:    High / Medium / Low — <one-line why>

KEY FINDINGS
  1. <claim> [source, date]
  2. <claim> [source, date]   (note: sources disagree — see below)
  ...

REASONING / INFERENCE
  <what you concluded from the facts, with assumptions named>

CONTEXT & CAVEATS
  <scope, definitions, anything that bounds the answer>

UNKNOWNS / WHAT WOULD CHANGE THIS
  <gaps you couldn't fill; what to verify before acting>

SOURCES
  [1] <title> — <publisher/author>, <date>, <url> — primary/secondary, why trusted
  ...

Anti-patterns

• Confirmation hunting — searching only for what you hope is true. Search the opposite.
• Citation laundering — three articles citing one origin presented as three sources.
• Stale-fact trap — quoting a "current" superlative that quietly expired.
• Burying the answer — making the reader mine paragraphs for the conclusion.
• False precision — "$4,231,847" from a source that said "about $4M".
• Unlabeled inference — presenting your reasoning as if it were a sourced fact.
• Over-hedging — refusing to give an answer when one is warranted. Calibrate, don't dodge.