Go deeper without losing structure.
Define the research boundaries first, then investigate the topic through evidence, stakeholders, drivers, competing perspectives, practical implications and uncertainty.
Act as an experienced research analyst and subject-matter investigator. TASK: Conduct a structured deep-dive analysis of the topic below. TOPIC: [Enter the topic.] RESEARCH GOAL: [What do you want to understand, decide or explain?] AUDIENCE: [Who will use the research?] CONTEXT: [Why does this topic matter?] GEOGRAPHIC SCOPE: [Country, region, global or not applicable.] TIME SCOPE: [Current, historical period, last 5 years, future outlook, etc.] KNOWN INFORMATION: [List anything already known.] SPECIFIC QUESTIONS: [List questions that must be answered.] AREAS TO INCLUDE: [List any required themes.] AREAS TO EXCLUDE: [List anything outside the scope.] RESEARCH DEPTH: [Standard / Deep / Expert overview] OUTPUT PURPOSE: [Report, article, presentation, business decision, learning, strategy, etc.] DEEP-DIVE REQUIREMENTS: 1. DEFINE THE TOPIC Start by clearly defining: - what the topic is - important terminology - boundaries of the subject - common misunderstandings - related concepts that should not be confused with it 2. CONTEXT & BACKGROUND Explain: - how the topic developed - why it matters - important historical or structural context - major events or changes that shaped it Include only background that helps explain the present topic. 3. CURRENT STATE Describe the current situation. Where relevant include: - major developments - current practices - adoption - market conditions - technology - regulation - public behavior - operational realities Clearly identify information that requires current verification. 4. KEY COMPONENTS Break the topic into its most important components. For each component explain: - what it is - how it works - why it matters - how it connects to the broader topic 5. KEY STAKEHOLDERS Identify important groups such as: - customers - companies - governments - regulators - workers - researchers - suppliers - platforms - communities Explain their roles, interests and influence. 6. MAIN DRIVERS Identify forces shaping the topic. Examples: - economics - technology - customer demand - demographics - regulation - competition - infrastructure - culture - environmental factors Distinguish strong evidence from speculation. 7. DATA & EVIDENCE Identify the most useful quantitative and qualitative evidence. Where relevant include: - market data - adoption rates - statistics - trends - survey findings - financial indicators - research findings - operational evidence For every important number, identify the source and date where available. Do not invent statistics. 8. MAJOR PERSPECTIVES Explain credible competing interpretations or viewpoints. For each perspective: - summarize the argument - identify supporting evidence - identify limitations - explain where disagreement exists Do not create artificial balance when evidence strongly favors one conclusion. 9. BENEFITS / OPPORTUNITIES Identify realistic advantages or opportunities associated with the topic. Explain: - who benefits - under what conditions - evidence supporting the benefit - limitations 10. RISKS / CHALLENGES Identify major: - risks - barriers - disadvantages - unintended consequences - implementation challenges Classify important risks as: - High - Medium - Low Explain the reasoning. 11. COMMON CLAIMS Identify frequently repeated claims about the topic. Classify each as: - Well supported - Partially supported - Disputed - Unsupported - Requires current verification Explain briefly. 12. MYTHS & MISCONCEPTIONS Identify important misconceptions. Explain: - why the misconception exists - what the evidence actually supports - what remains uncertain 13. CASE EXAMPLES Where useful, include real examples or case studies. For each: - explain the situation - identify what happened - explain why it is relevant - avoid generalizing from one example 14. COMPARISONS Compare relevant: - approaches - technologies - models - countries - companies - strategies - alternatives Use clear criteria rather than vague statements. 15. REGULATION / POLICY If relevant, explain: - applicable rules - major regulatory bodies - current policy direction - compliance considerations - important differences by jurisdiction Do not give legal conclusions beyond the available evidence. 16. ECONOMICS Where relevant analyze: - costs - incentives - revenue models - affordability - productivity - investment - economic impact Separate known figures from estimates. 17. TECHNOLOGY Where relevant analyze: - technologies involved - level of maturity - limitations - dependencies - adoption barriers - likely developments Avoid hype. 18. PRACTICAL IMPLICATIONS Explain what the topic means in practice for the intended audience. Focus on: - decisions - actions - trade-offs - operational impact - realistic constraints 19. TRENDS Identify important trends. For each trend state: - direction - evidence - drivers - uncertainty - likely significance 20. FUTURE OUTLOOK Discuss plausible future developments. Separate: - highly likely developments - reasonable possibilities - speculative scenarios Do not present forecasts as facts. 21. KNOWLEDGE GAPS Identify: - unanswered questions - weak evidence areas - conflicting findings - unavailable data - topics needing more research 22. SOURCE QUALITY Prioritize: - primary sources - government sources - regulators - academic research - original company information - reputable research institutions - established news sources Treat promotional material, social media and unsourced claims cautiously. 23. CONTRADICTIONS If credible evidence conflicts: - present both findings - compare dates - compare methodology - compare scope - explain the likely reason for the disagreement 24. CONFIDENCE For major conclusions assign: - High confidence - Medium confidence - Low confidence Explain significant uncertainty. 25. FINAL SYNTHESIS Summarize: - what is most important - what is strongly established - what is commonly misunderstood - biggest opportunity - biggest risk - biggest uncertainty - what should be researched next OUTPUT FORMAT: 1. Executive Overview 2. Topic Definition 3. Background 4. Current State 5. Key Components 6. Stakeholders 7. Main Drivers 8. Evidence & Data 9. Major Perspectives 10. Opportunities 11. Risks & Challenges 12. Common Claims 13. Myths & Misconceptions 14. Case Examples 15. Comparisons 16. Regulation / Policy 17. Economics 18. Technology 19. Practical Implications 20. Trends 21. Future Outlook 22. Knowledge Gaps 23. Contradictory Evidence 24. Confidence Assessment 25. Final Synthesis 26. Recommended Further Research IMPORTANT: - Do not invent facts, statistics, sources or quotations. - Clearly distinguish facts, estimates, interpretations and forecasts. - Use current evidence when the topic changes quickly. - Prefer original and authoritative sources. - State when evidence is weak, conflicting or unavailable. - Do not hide uncertainty. - Keep the analysis focused on the stated research goal rather than producing an encyclopedia-style overview.
Use the prompt effectively.
Define the scope first
A deep dive can quickly become too broad. Set the geography, time period, audience and questions before starting.
Break the topic into systems
Look at stakeholders, drivers, evidence, risks, trends and practical implications rather than collecting disconnected facts.
Compare viewpoints carefully
Include meaningful disagreement when it exists, but do not manufacture equal weight for poorly supported positions.
End with uncertainty
A strong deep dive should show what is established, what is disputed and what still needs investigation.
Turn a broad subject into structured investigation.
Topic: AI adoption in small businesses.
Goal: Understand where AI is delivering practical value and where adoption barriers remain.
Audience: Small-business owners.
Scope: Current global overview with emphasis on practical business use.
Questions: Which use cases are most common, what benefits are supported by evidence, what risks matter most and what prevents adoption?
The research should distinguish actual AI adoption from general awareness or experimentation.
Key areas should include customer service, content creation, administration, analytics, automation and software-assisted decision support.
The analysis should compare evidence on productivity benefits with barriers such as cost, skills, data quality, privacy concerns and workflow integration.
Future claims should be separated into established trends and speculative expectations.
The final synthesis should identify which use cases appear mature enough for practical small-business adoption and where evidence remains limited.
Create stronger deep-dive research.
Use a research question, not just a topic
A focused question such as 'How is AI changing small-business inventory management?' usually produces stronger research than simply requesting a deep dive on AI.
Separate current state from outlook
What exists today and what may happen in the future require different kinds of evidence.
Track confidence
Not every conclusion deserves the same certainty. Confidence labels help prevent weak evidence from appearing as established fact.