Persona Intelligence: The Missing Dimension That’s Costing Your AI Research Its Competitive Edge

Want to improve your research and content creation? Start by changing your perspective.

Imagine five people trying to solve a problem. One might stick to their viewpoint, while the others might explore multiple angles, different roles, experiences, and contexts. Guess who finds better solutions? The one who seizes diverse perspectives.

In research and content creation, perspective is everything. Yet, large language models (LLMs) like ChatGPT often provide a single, streamlined answer designed to please rather than challenge your thinking. This can limit the depth of your understanding. It’s not the fault of the LLM, but our awakening to implement it into our writing.

That’s where persona-based prompting comes in. By instructing the AI to respond as different personas, such as a first-person user (You), a third-person expert or experts, or even a specific professional like a marketer or scientist, you can simulate a team meeting where diverse opinions are shared. This approach, inspired by techniques like perspective-transition prompting, helps you see a problem from multiple angles, much like a boardroom discussion where each participant brings a unique viewpoint.

This newsletter will explore how persona-based prompting can improve your research and writing. To support this approach, we’ll provide practical steps, real-world examples, and fascinating studies from prompt engineering research. Whether you’re analyzing data, writing content, or solving complex problems, personas can enrich deeper insights and more balanced outcomes.

What Are Personas in Prompting?

Personas in prompting refer to specific roles, characters, or viewpoints that you instruct the AI to adopt when generating responses. By defining a persona, you guide the AI to think and respond as if it were that character, providing insights tailored to that perspective.

For example:

• First-person persona: A user, customer, or stakeholder directly involved in the scenario, offering a personal or emotional viewpoint.
• Third-person persona: An expert, analyst, or critic with a broader, objective perspective, focusing on data or strategy.
• Creative personas: Professionals like a marketer, scientist, or historian, or even fictional characters, to bring unique angles to the problem.

Using personas, you can uniquely shape the AI’s responses to align with different viewpoints, providing more contextually relevant and diverse answers. This technique is particularly valuable in scenarios requiring multiple perspectives, such as research, content creation, debate simulations, or strategic decision-making.

The concept draws from prompt engineering techniques like perspective prompting, where the AI is guided to respond from specific roles or opinions. By assigning personas, you create a virtual “team meeting” where the AI acts as multiple contributors, each offering a distinct take on the problem. It’s a subtle adjustment, yet it adds a milestone in your deliveries.

Why Use Personas?

Incorporating multiple personas into your prompting offers several benefits, making it a powerful tool for researchers and content creators:

• Reduced Bias: A single perspective can be skewed by personal assumptions or limited data. Multiple personas help balance these biases, leading to fairer and more objective conclusions.
• Adds Substance in your Insights: Different viewpoints reveal aspects of a problem you might not have considered, sparking innovation and creativity.
• Enhanced Content Quality: Whether you’re writing a blog, research paper, or newsletter, diverse perspectives make your work more engaging, credible, and relatable to a broader audience.
• Better Problem-Solving: Just as a diverse team outperforms a solo thinker, persona-based prompting leverages AI to simulate a collaborative brainstorming session, leading to more robust solutions.
• Tailor-made answers to the real official personas of the industry: Do not just pick a generic role of Compliance officer, name them! You would be surprised how much content is out there, and I can learn about the personas and help you write the response based on your target.

This approach is especially valuable in today’s complex world, where problems rarely have a single “right” answer. By mimicking the dynamics of a team meeting, persona-based prompting allows you to harness the power of AI to think more critically and comprehensively.

The idea of using AI as a “team” aligns with the user’s analogy of chat-based LLMs acting like a boardroom where different personas contribute. This collaborative framework can transform how you approach research, making it more dynamic and insightful.

How to Use Personas in Prompting

Ready to try persona-based prompting? Here’s a step-by-step guide to implementing it effectively:

1. Define Your Personas: Identify the perspectives you want the AI to adopt. Common choices include CFO vs. COO, compliance Officer vs. R&D Officer vs. CMO, rigor vs. innovation vs. market growth, and compliance.
2. Craft a Clear Prompt: Structure your prompt to explicitly request responses from each persona, followed by a synthesis of the perspectives. For example:
3. Review and Refine: Analyze the AI’s responses to see how the perspectives align or differ. If the output lacks depth or clarity, refine the prompt by adding more context or adjusting the personas.

Practical Tips for Effective Prompts

To maximize the benefits of persona-based prompting, keep these tips in mind:

• Start Simple: Begin with two personas, like a first-person user and a third-person expert, before experimenting with more complex roles.
• Be Specific: Clearly define each persona’s role or viewpoint in the prompt (e.g., “as a marketer with 10 years of experience”).
• Use a Structured Format: Follow the “answer, critique, merge” structure to ensure clarity and coherence.
• Iterate and Refine: If the AI’s response isn’t detailed enough, tweak the prompt to include more context or additional personas.
• Experiment with Applications: Try this technique in different contexts, such as brainstorming, research, or content planning, to see where it shines.

The best way to demonstrate the difference is to first show you a prompt without considering personas, and then an example with the personas.

Please use this to enhance your decision-making, but not as a replacement for your cognitive thinking! I often hear, AI is stupid and today people do not think for themselves. AI is a tool, luckily, it needs our cognitive and critical thinking to make the best decisions.

Persona-Based Prompting Examples for PrecisionGear Inc.

Persona-based prompting enhances AI-assisted research and decision-making by instructing large language models (LLMs) to respond from specific roles, such as a CFO, COO, R&D officer, or compliance officer. This approach simulates a dynamic boardroom discussion, where diverse perspectives converge to address complex business challenges. Below, we provide two examples to illustrate the difference between a generic prompt and a persona-based prompt when tackling a critical question for PrecisionGear Inc., a multinational company manufacturing high-precision gear components for the automotive, aerospace, and renewable energy sectors, with production facilities primarily in China and Thailand (fictitious company).

PrecisionGear Inc. is navigating significant challenges in 2025 due to:

• Tariffs: The US has imposed tariffs of up to 54% on Chinese goods and 10-20% on Thai exports, significantly increasing production costs (CNBC Tariffs Survey).
• Supply Chain Disruptions: Geopolitical tensions and port congestion have led to a 30% increase in lead times and a 15% rise in logistics costs (Oxford Economics Tariffs).
• Economic Instability: Inflation, fluctuating energy prices, and reduced demand in key markets like Europe add financial pressure (J.P. Morgan Tariffs).

The board is split on whether to prioritize improving EBITDA through cost optimization or investing in innovation to develop advanced gear technologies or alternative manufacturing processes to mitigate supply chain risks. These examples demonstrate how persona-based prompting delivers deeper, context-specific insights compared to a generic approach, tailored to PrecisionGear’s industry and economic landscape.

Example 1: Generic Prompt Without Role-Playing

When an LLM is queried without specifying personas, it typically provides a broad, balanced response that lacks the specificity required for nuanced decision-making. This example highlights the limitations of a generic prompt.

Prompt: “Should PrecisionGear Inc. focus on improving EBITDA or invest in innovation amidst tariffs and supply chain disruptions in the gear manufacturing industry?”

Response: PrecisionGear Inc. should strive to balance improving EBITDA and investing in innovation. Focusing on EBITDA ensures financial stability, which is crucial given the cost increases from tariffs and supply chain disruptions. However, innovation is vital for long-term growth, such as developing new gear technologies or processes to reduce reliance on current manufacturing locations. A balanced strategy that allocates resources to both areas would be optimal, depending on the company’s financial health and market dynamics.

Analysis: This response is vague and lacks actionable insights. It acknowledges the dual priorities but fails to address specific challenges, such as the financial impact of tariffs, operational complexities of supply chain disruptions, or regulatory considerations in the gear manufacturing industry. It offers a high-level overview that does not fully grapple with PrecisionGear’s unique context.

Example 2: Persona-Based Prompt With Role-Playing

Using persona-based prompting, we instruct the LLM to respond from the perspectives of a CFO, COO, R&D officer, and compliance officer, each representing distinct priorities: financial rigor, operational efficiency, innovation, and compliance. These responses are tailored to PrecisionGear’s context, considering the gear manufacturing industry, tariffs, supply chain issues, and economic instability. We then synthesize these perspectives into a cohesive conclusion, showcasing the depth and nuance that personas bring to AI-assisted research.

Prompt: “PrecisionGear Inc. is a multinational company manufacturing high-precision gear components for the automotive, aerospace, and renewable energy sectors, with production facilities primarily in China and Thailand. In 2025, the company faces US tariffs of up to 54% on Chinese goods and 10-20% on Thai exports, alongside supply chain disruptions causing a 30% increase in lead times and a 15% rise in logistics costs. Economic instability, including inflation and reduced demand in key markets, adds further pressure. The board is divided on whether to focus on improving EBITDA by optimizing current operations and reducing costs or to invest in innovation to develop advanced gear technologies (e.g., lightweight composites, smart gears) or alternative manufacturing processes (e.g., additive manufacturing) to mitigate supply chain risks.

Please respond as the following personas, each providing a detailed perspective based on their role and priorities in this specific context. After each persona’s response, provide a brief critique from the perspective of another persona. Finally, synthesize all perspectives into a cohesive conclusion.

• CFO: Focus on financial rigor, emphasizing the impact of tariffs (e.g., 54% on Chinese goods, 10-20% on Thai exports), the need to maintain cash flow, and the risks of investing in innovation during economic uncertainty.
• COO: Focus on operational efficiency, addressing supply chain disruptions (e.g., 30% increase in lead times), the feasibility of relocating manufacturing, and how innovation could enhance operational resilience.
• R&D Officer: Focus on innovation, advocating for advanced gear technologies (e.g., lightweight composites, smart gears with sensors) or processes (e.g., 3D printing) to reduce dependency on tariff-affected regions and capture new markets.
• Compliance Officer: Focus on compliance, highlighting regulatory challenges of operating in multiple countries, including trade laws (e.g., USMCA rules of origin) and environmental regulations for new technologies.”

Response:

• CFO Perspective: As the CFO of PrecisionGear Inc., my priority is safeguarding financial stability in a turbulent economic environment. The US tariffs—54% on Chinese goods and 10-20% on Thai exports—have increased our production costs by an estimated 25% across our product lines (CNBC Tariffs Survey). Coupled with a 15% rise in logistics costs and reduced demand in Europe due to inflation, our EBITDA margins are under severe pressure (J.P. Morgan Tariffs). Investing in innovation, such as lightweight composite gears or additive manufacturing, requires significant capital expenditure with returns that may not materialize for 3-5 years, a risky proposition given current cash flow constraints. I recommend focusing on EBITDA improvement through aggressive cost-cutting: renegotiating supplier contracts to offset tariff costs, reducing workforce overtime, and optimizing inventory to minimize holding costs. We could also explore passing a portion of tariff costs to customers, though this risks losing market share in price-sensitive segments like automotive. Relocating manufacturing to tariff-friendly regions like Mexico is tempting but involves high setup costs and long lead times, making it a secondary priority.
• COO Perspective: As the COO, I’m tackling supply chain disruptions that have increased lead times by 30% and logistics costs by 15%, threatening our ability to meet delivery schedules for aerospace and automotive clients (Oxford Economics Tariffs). Our reliance on China and Thailand exposes us to ongoing tariff and geopolitical risks. One solution is to diversify manufacturing by establishing a facility in Mexico, leveraging USMCA’s lower tariffs, or partnering with suppliers in Malaysia, where costs are competitive. However, relocation involves capital investment, regulatory hurdles, and retraining workforces, which could take 18-24 months. Innovation offers a complementary path: adopting additive manufacturing (3D printing) could enable on-demand production closer to key markets, reducing lead times and tariff exposure. Similarly, AI-driven supply chain analytics could optimize logistics. I propose a dual strategy: streamline current operations to boost EBITDA (e.g., consolidating production lines) while investing in targeted innovations to enhance supply chain resilience.
• R&D Officer Perspective: As the R&D Officer, I see innovation as PrecisionGear Inc.’s path to long-term survival and market leadership. Tariffs and supply chain disruptions highlight the vulnerability of our Asia-centric manufacturing model. Investing in advanced gear technologies, such as lightweight composite gears for aerospace or smart gears with embedded sensors for renewable energy applications, could differentiate us in high-growth markets like electric vehicles and wind turbines. For example, composite gears reduce weight by 30%, improving fuel efficiency in automotive applications. Additionally, additive manufacturing (3D printing) could enable localized production in North America or Europe, bypassing tariffs and reducing lead times. While these initiatives require upfront investment, they position us to capture premium markets and reduce long-term costs. Delaying innovation risks losing ground to competitors like GearTech, which recently launched a smart gear line. I advocate for a dedicated R&D budget, even if limited to 5% of revenue, to prioritize high-impact projects with 2-3 year payback periods.
• Compliance Officer Perspective: As the Compliance Officer, my role is to ensure PrecisionGear Inc. navigates the regulatory complexities of global operations. The US tariffs and evolving trade agreements, such as USMCA’s rules of origin, impose strict requirements on sourcing and manufacturing (Washington Post Tariffs). Relocating to Mexico or Malaysia requires compliance with local labor, safety, and environmental regulations, which vary significantly. For instance, Malaysia’s environmental standards for manufacturing are tightening, potentially increasing costs. Innovation introduces additional risks: new composite materials must meet aerospace certifications (e.g., FAA standards), and smart gears with sensors must comply with data privacy laws like GDPR in Europe. I recommend establishing a compliance task force to conduct risk assessments for any relocation or R&D initiatives, ensuring alignment with international trade and industry regulations. While EBITDA-focused strategies pose fewer compliance risks, all decisions must prioritize regulatory adherence to avoid fines or reputational damage.
• Synthesis: The perspectives of the CFO, COO, R&D Officer, and Compliance Officer reveal the intricate trade-offs PrecisionGear Inc. faces in balancing EBITDA and innovation. The CFO’s focus on financial rigor underscores the need to offset tariff-driven cost increases (up to 54%) through cost optimization, ensuring cash flow stability. The COO’s dual strategy of operational streamlining and targeted innovation addresses supply chain disruptions (30% lead time increases) while building resilience. The R&D Officer’s push for advanced gear technologies and additive manufacturing positions the company for future growth in high-value markets. The Compliance Officer’s emphasis on regulatory adherence ensures that all strategies mitigate legal and reputational risks.

Analysis: The persona-based response delivers a highly detailed, context-specific analysis that far surpasses the generic prompt. Each persona—representing financial rigor (CFO), operational efficiency (COO), innovation (R&D Officer), and compliance—offers tailored insights, reflecting the real-world complexities of the gear manufacturing industry. Critiques challenge assumptions, fostering a robust synthesis that addresses tariffs, supply chain disruptions, and economic instability. The response proposes actionable strategies, such as phased relocation and targeted R&D, demonstrating how LLMs can enhance strategic decision-making in high-stakes scenarios.

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Supporting Research

The value of persona-based prompting is backed by cutting-edge research in AI and prompt engineering. Below, we highlight key studies and resources that support this approach, providing a robust foundation for its application in research and content creation.

Multi-Prompt and Multi-Turn Prompting

A 2023 study, A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models by Song et al., explores advanced prompting techniques for LLMs (Prompt Engineering Study). The authors describe multi-prompt engineering, where multiple prompts are used to cover a broader semantic space, and multi-turn prompt engineering, where tasks are broken into sub-tasks, each potentially representing a different perspective.

For example, multi-prompt engineering might involve asking the same question in different ways (e.g., “What are the benefits of exercise?” vs. “How does exercise impact health?”) and aggregating the responses for a more complete answer. Multi-turn prompting decomposes complex tasks into steps, such as answering from one persona, critiquing it from another, and synthesizing the results. These methods are particularly effective for reasoning and problem-solving, aligning closely with persona-based prompting.

The study highlights that multi-prompt techniques improve outcomes in natural language understanding (NLU) and generation (NLG), such as reasoning tasks and content creation. This suggests that persona-based prompting can enhance the reliability and depth of AI-assisted research by simulating diverse viewpoints.

DiVeRSe Prompting

Another relevant concept is DiVeRSe Prompting, outlined in a 2024 article by Relevance AI (DiVeRSe Prompting). This technique generates multiple prompts for a single query, each offering a unique perspective, and then synthesizes the responses through a verification process.

For instance, in scientific research, DiVeRSe Prompting can generate multiple hypotheses for a phenomenon, each backed by different theoretical frameworks. In business analytics, it analyzes consumer behavior from various angles, such as financial impact or market positioning. The process involves:

• Generating diverse prompts (e.g., M1 prompts with unique framings).
• Producing multiple reasoning paths for each prompt (e.g., M2 paths).
• Evaluating paths for logical consistency, relevance, and completeness.
• Synthesizing the top-ranked paths into a final answer.

An example from the article illustrates this with a legal analysis system. The AI generates interpretations based on constitutional implications, precedent-based analysis, statutory interpretation, and policy considerations, then evaluates each for accuracy and consistency before delivering a comprehensive answer. This mirrors the concept of persona-based prompting as a “team meeting” of personas, where each contributes a distinct perspective.

DiVeRSe Prompting has applications in:

• Scientific Research: Generating diverse hypotheses.
• Education: Creating varied assessment questions or learning materials.
• Business Analytics: Analyzing markets from multiple angles.
• Healthcare: Diagnosing symptoms through different medical frameworks.
• Software Development: Optimizing code or detecting bugs.

These applications highlight the versatility of multi-perspective prompting, making it a powerful tool for researchers and content creators.

Persona Prompting in Practice

Additional resources provide practical insights into persona-based prompting. A 2024 article by TutorialsPoint, Prompt Engineering Perspective Prompts, defines perspective prompting as crafting prompts to align AI responses with specific personas or roles (Perspective Prompts). For example, asking the AI to “explain blockchain as a technology expert” or “discuss renewable energy as an environmentalist” tailors responses to specific viewpoints. This supports the idea of defining clear personas to achieve more relevant and diverse outputs.

Similarly, a 2025 article from PromptHub Blog, Role-Prompting: Does Adding Personas to Your Prompts Really Make a Difference?, explores whether assigning personas improves AI performance (Role-Prompting). The article reviews studies and concludes that persona prompting is most effective when the personas are well-defined and relevant to the task, particularly in creative and analytical contexts.

A 2023 blog post by Ethan Mollick, Working with AI: Two Paths to Prompting, emphasizes the importance of structured prompts for better AI outcomes (Two Paths to Prompting). While not specifically about personas, Mollick’s discussion of testing prompts from the user’s perspective aligns with the need to consider diverse viewpoints in prompt design, reinforcing the value of persona-based approaches.

Community Insights

The use of personas is also popular in online communities. A 2023 X post in the r/ChatGPTPro subreddit asked for a collection of ChatGPT persona prompts, indicating community interest in this technique (ChatGPT Persona Prompts). Users shared examples like “act as a historian” or “respond as a customer service agent,” showing how personas are applied in practice to achieve tailored responses.

These studies and resources collectively demonstrate that persona-based prompting is a well-established and effective technique in prompt engineering, supported by both academic research and practical applications.

A Final Reflection: The Beauty of Process

Persona-based prompting is a high-value approach to research and content creation. By leveraging AI to simulate a diverse team of perspectives, you can produce more insightful work, with substance, balanced, and impactful. Whether analyzing data, writing a blog, or solving a complex problem, this technique helps you think more critically and creatively.

Happy strategizing,
Stay Visionary, Lead with an Edge!

P.S. This newsletter aims to provoke thought among leaders by blending historical context with forward-thinking analysis. It presents AI not as a threat but as an evolution in the dynamic world of digital influence.