Strong leadership begins with decisions grounded in robust research. In an age where GenAI can surface oceans of data in minutes, the real superpower lies in asking the right question with surgical precision. A great Deep Research prompt doesn’t mean adding more words; it means structuring your request, so the results are focused, actionable, and decision-ready, whether you’re in government, enterprise, or the not-for-profit sector.

Why Prompt Structure Matters

A vague prompt produces generic output: long, unfocused, and of little use to time-poor leaders. By contrast, a well-structured prompt guides GenAI (and your team) to generate clear, credible, and strategically relevant insights, the kind that fuel bold moves in digital strategy, technology adoption, or business continuity.

The secret? Clarity, context, and actionable intent; without over-instructing the model’s reasoning process. For advanced “reasoning-first” models, it’s best to define the goal, constraints, and output format, then let the model determine the best path to get there.

Five Steps to Structuring a Deep Research Prompt

1. Define the “why” and audience

Begin by stating why the research matters and who needs the answer. Is it to inform a C-suite decision, support a funding bid, or shape a new digital policy for Queensland? Make this explicit so results align with stakeholder priorities and are pitched at the right level (e.g., concise summary for executives, detailed breakdown for technical teams).

2. Frame the Scope and Context – but stay lean

Spell out the primary question and any related issues worth exploring. Identify local or regulatory factors (e.g., Queensland legislation) that must be considered. Keep context essential, not exhaustive. Reasoning models often perform better with only the most relevant details and permission to ask for more.

3. Detail output requirements

Be specific about the desired structure. Should it be a one-page executive summary, a comparative table, or a risk - opportunity matrix? The clearer you are, the more “delivery-ready” the output will be. Match the style to the audience: formal for board packs, visual for presentations, bullet points for rapid briefings.

4. Set time, source, and credibility parameters

State relevant timeframes. Are you after current trends (past six months), historical context, or future forecasts? Identify preferred or trusted source types (government reports, local think tanks, peer-reviewed journals). If accuracy matters, instruct the model to quantify confidence levels for each major claim and request clarification if critical information is missing.

5. Push for depth, breadth, and strategic value

Go beyond “just the facts.” Ask for patterns, contradictions, and strategic implications. For higher value, invite the model to explore multiple possible conclusions before recommending the most viable option. This “parallel before convergence” approach can uncover opportunities or risks that a single-line-of-reasoning answer might miss.

Example: From Generic to Decision-Ready

Generic Prompt

“What digital mental health programs exist for young people?”

High-Quality, Structured Prompt

“Prepare a decision-ready report for Queensland Health executives summarising the top three evidence-based digital mental health programs for 12-25-year-olds in regional Queensland. Compare effectiveness, scalability, and local uptake trends (2022-2025). Identify a key risk or barrier for each and recommend next steps for state-wide implementation, citing Queensland Health, Beyond Blue, and national research. Indicate confidence levels for each major claim, and request clarification if essential data is missing.”

This second version anchors the request in context, action, and strategic usability, and leaves the reasoning process to the model while ensuring outputs are credible and immediately deployable.

The Power of Negative Examples

Sometimes the easiest way to illustrate what makes a good Deep Research prompt is to show what a poor one looks like, and why it fails.

Weak Prompt Example

“Summarise everything you know about mental health in Australia.”

Why this misses the mark:

• Overly broad – no defined boundaries or scope, so the result could be thousands of words of unfocused content.
• No audience in mind – without knowing who it’s for, the output may be too technical, too simplistic, or simply irrelevant.
• No actionable focus – the request doesn’t lead to decision-ready insights; it invites a generic information dump.

By contrast, the structured Queensland Health example in the previous section shows how adding audience, scope, timeframe, sources, and action requirements transforms the same general topic into a concise, relevant, and usable output.

Make It Iterative

Even the best prompt is the start of a conversation, not the end. Use the first output to spot gaps, then refine:

• “Add a comparative table summarising costs and benefits.”
• “Condense this into a 200-word brief for the Minister.”

Multi-turn refinement lets you calibrate for precision without overloading the initial prompt.

Take Action

Structured prompts transform how your organisation uses GenAI for research. They turn information into competitive advantage and clarity into action, especially when you combine clear goals, lean context, and output-ready formats.

About the Author 

This article was crafted by Ben Scown, Head of Strategy and Advisory at Integral.

Ben made use of several AI tools in researching this article and combined this learning with his own first hand and deep knowledge and experience as a specialist in AI technology.

  Connect with Ben Scown on LinkedIn.

  For more real-world tips and tailored digital research solutions, visit www.integral.com.au because every great innovation begins with the right question.