Deep Research Report Generator

Create source-backed deep research reports with clear search plans, evidence tables, contradiction checks, and executive recommendations.

The Prompt

You are a senior research analyst. Build a deep research report that is evidence-led, explicit about uncertainty, and easy for decision-makers to act on.

## 1. Frame the Research Objective
- Restate the objective in one sentence
- Define the decision this research should support
- List the scope boundaries
- List assumptions that need validation

## 2. Create a Search Plan
- Break the topic into 4-8 research questions
- Recommend source types for each question:
  - Official company pages
  - Product docs
  - Regulatory or government sources
  - Earnings reports or investor materials
  - Reputable news coverage
  - Analyst or industry reports
- Note what evidence would count as strong, medium, or weak

## 3. Collect and Organize Evidence
Build an evidence table with:
- Research question
- Source name
- Source URL or citation label
- Publication date
- Key claim
- Evidence strength
- Relevance to objective

## 4. Detect Contradictions and Gaps
- Identify conflicting claims
- Explain which source appears more reliable and why
- Flag missing data that materially changes confidence
- List open questions for follow-up research

## 5. Synthesize Findings
Provide:
- Executive summary
- 5-7 key findings
- Implications for the target audience
- Risks and downside scenarios
- Opportunities and strategic recommendations

## 6. Separate Facts From Inference
Create two sections:
- Confirmed facts
- Inferences / judgment calls

For every inference, state the reasoning and confidence level.

## 7. Output Format
Return the final answer in this structure:
1. Research objective
2. Scope and assumptions
3. Search plan
4. Evidence table
5. Key findings
6. Contradictions and gaps
7. Recommendations
8. What to research next

## Quality Rules
- Prefer recent and primary sources when possible
- Do not hide uncertainty
- Mark outdated or weak evidence clearly
- Do not fabricate numbers, quotes, or citations
- If the request is underspecified, begin by listing the 3 most important clarifying assumptions

Research topic: [TOPIC]
Decision to support: [DECISION]
Audience: [EXECUTIVE / PM / FOUNDER / INVESTOR / OPERATIONS]
Geography: [MARKET]
Time horizon: [DATE RANGE]
Preferred sources: [OPTIONAL]
Output depth: [BRIEF / STANDARD / DEEP]

How to Use This Prompt

1. Give the model a real decision, not just a broad topic.
2. Specify geography and time horizon so the evidence stays relevant.
3. Ask for contradiction checks when researching markets with hype or noisy coverage.
4. Use the evidence table to spot what still needs manual verification.
5. Run a second pass focused only on the highest-risk assumptions.

Example Input

Research topic: AI intake and workflow automation tools for small US law firms
Decision to support: Should we build a SaaS product in this category?
Audience: Founder
Geography: United States
Time horizon: Last 18 months
Preferred sources: Product websites, legal tech publications, pricing pages, review sites
Output depth: Deep

Example Output

1. Research Objective

Evaluate whether AI intake and workflow automation for small US law firms is attractive enough to justify an MVP.

2. Scope and Assumptions

• Scope: Firms with 1-20 attorneys
• Assumption: Buyers care more about time savings and intake conversion than advanced analytics
• Assumption: Adoption depends on integration with existing CRM or case management systems

3. Search Plan

• Market size and demand indicators
• Current vendor landscape and positioning
• Pricing and packaging patterns
• Workflow pain points in intake, follow-up, and document collection
• Compliance or trust barriers

4. Evidence Table

5. Key Findings

1. The category is crowded on generic intake software, but lighter AI-assisted workflow products remain differentiated.
2. Speed-to-response and staff time savings are the strongest recurring value claims.
3. Trust, privacy, and data handling remain primary adoption blockers.
4. Bundled integrations appear to matter more than raw AI sophistication for this segment.

6. Contradictions and Gaps

• Some vendors market fully automated intake, but case studies often describe assisted rather than autonomous workflows.
• There is limited public evidence on renewal rates for small-firm AI products.

7. Recommendations

• Start with intake follow-up, document collection, and lead qualification instead of full legal workflow automation.
• Position around operational throughput and conversion lift, not “AI magic.”
• Validate 10-15 interviews with firms already using modern intake tools before building.

8. What to Research Next

• Willingness to pay by firm size
• Required integrations for the top 3 target practice areas
• Privacy and audit expectations in buyer conversations

Practical Variations for Deep Research Report Generator

• Diagnosis mode: Ask whether the real constraint is demand, positioning, operations, pricing, distribution, or execution.
• Options mode: Request three options with cost, upside, downside, risk, and evidence needed.
• Execution mode: Ask for a 30-day plan with owner, metric, checkpoint, and rollback condition.

Follow-Up Prompts

Use these after the first answer:

• “Rewrite this using only the context I provided. Label assumptions instead of hiding them.”
• “Give me a conservative version, a direct version, and a version optimized for speed.”
• “Create a final review checklist I can use before I publish, send, ship, or present this.”

What Makes This Page Different

This page is useful when you are working on deep research report generator and need more than a blank chat box. It gives you a starting prompt, context checklist, review criteria, and practical variations so the answer can be tested instead of merely accepted. If your task is broader, start with a workflow guide first, then come back to this prompt once the input, audience, and success criteria are clear.