How to do deep research with AI

AI can make research dramatically faster, but speed is only useful when the result remains traceable. A defensible report lets a reader see which claims came from sources, which conclusions came from analysis, and where the available evidence is incomplete or contested.

The process below works for market research, literature reviews, policy briefs, product decisions, technical investigations, and any question where “the AI said so” is not an acceptable source.

What deep research actually means

Deep research combines question decomposition, source discovery, evidence extraction, comparison, synthesis, and citation checking. The final report is only the visible result. Most of the quality comes from what happens before the prose is written.

A normal chatbot answer often moves directly from a broad question to a confident summary. A research workflow pauses between stages and produces intermediate artifacts: a scope, search plan, source list, evidence table, unresolved questions, outline, and citation audit.

The seven-step deep research process

Build an evidence table before writing

An evidence table stops citations from becoming decoration. It also exposes when ten articles are repeating the same original source, which prevents the illusion of independent confirmation.

• Source: title, publisher or journal, date, and URL or DOI.
• Source type: primary research, official data, company material, review, or commentary.
• Supported claim: the narrow statement this source actually supports.
• Method and scope: sample, geography, time period, and research design when relevant.
• Limitations: conflicts of interest, missing data, small samples, or uncertain generalizability.
• Evidence direction: supports, contradicts, or complicates the working conclusion.

AimiChat’s Deep Research mode searches multiple academic and web sources and produces citation-linked research. For high-stakes claims, follow it with AimiVerify or a manual review of the underlying source.

A reusable deep research prompt

Research question: [specific question]
Decision this report will support: [decision]
Audience: [reader]
Geography and time period: [scope]
Required source types: [primary studies, official data, documentation]
Excluded source types: [unsourced summaries, affiliate pages, etc.]

Before writing the report:
1. Decompose the question into independent research paths.
2. Search for supporting and contradictory evidence.
3. Build an evidence table with source, date, supported claim, method, and limitations.
4. Identify important gaps and disagreements.

Then write a report that clearly separates:
- established evidence,
- reasonable interpretation,
- unresolved uncertainty,
- and recommendations.

How to audit the finished report

Open every important citation

Confirm that the page exists, the cited information appears in it, and the source is not merely quoting a different source you should cite directly.

Read the sentence before and after the citation

A citation may support one number but not the broader conclusion wrapped around it. Narrow the sentence or add evidence when the prose goes beyond the source.

Check dates and version-sensitive claims

Prices, model availability, laws, software behavior, market share, and company roles can change quickly. Record access dates and prefer current primary material.

Search for counter-evidence

Ask what evidence would make the conclusion weaker. A report becomes more useful when it explains the conditions under which its recommendation may not hold.

Worked example: research whether a four-day week improves retention

A weak request—“Research the benefits of a four-day workweek”—invites a one-sided list. A useful protocol begins with a decision: should a 70-person software company run a six-month pilot? Define the population, outcome, geography, time horizon, and acceptable evidence before searching.

1. Operationalize the outcome: voluntary turnover, intent to leave, sick days, delivery performance, and employee-reported burnout are different measures.
2. Split the paths: retention evidence, productivity evidence, implementation conditions, sector differences, and failure cases.
3. Set the source policy: controlled trials and longitudinal studies first; official pilot evaluations and direct organizational data second; surveys and commentary for context.
4. Extract methods: Was pay held constant? Did hours fall or compress? Was participation voluntary? How long did follow-up last? Was there a comparison group?
5. Write the decision: recommend a reversible pilot only if the available evidence fits the company context, then define success and stop conditions.

The report should not say “four-day weeks increase retention” because several programs with different designs are placed under one label. It should say which implementations, populations, and outcomes the available evidence supports.

Assess evidence quality instead of ranking sources by appearance

When studies disagree, do not average the prose. Explain whether they tested different populations, definitions, interventions, methods, or dates. That explanation is often more decision-useful than a forced universal verdict.

Research standards behind the workflow

The PRISMA 2020 statement provides a model for reporting search, selection, and synthesis transparently. The Cochrane Handbook shows why risk-of-bias assessment belongs beside extraction. AI adds another failure mode: studies have documented fabricated bibliographic citations, and NIST's Generative AI Profile treats confident false content and citations as a core risk. This is why every important citation must be opened and matched to the claim.

Know when AI research is not enough

AI can organize a large research surface, but it does not remove the need for domain judgment. Medical, legal, financial, safety-critical, or publication-grade research should be reviewed by a qualified person who can evaluate methods and consequences.

Some sources may be inaccessible, unpublished, paywalled, badly indexed, or available only in datasets that require specialist analysis. A strong report names those limitations instead of pretending the search was exhaustive.