Mastering the Art of Prompting: A Literature-Informed Guide for Businesses and Content Creators

Mastering Prompting: Unlocking the Power of AI for Your Business

Introduction: Why Prompting Matters Now

Artificial intelligence is only as powerful as the instructions we give it. Large Language Models (LLMs), such as ChatGPT, can write, analyse, and create—but their performance hinges on the quality of user prompts. The difference between a vague query like “Tell me about AI” and a precise instruction like “Summarise AI, its subfields, and applications in healthcare and finance in under 150 words” is the difference between wasted time and actionable insight.

At Anova AI Labs, we believe businesses and creators must master this emerging skill to stay ahead. To provide practical guidance, we have reviewed the latest peer-reviewed research on prompt engineering published between 2023 and 2025. These studies offer evidence-based techniques that go beyond casual tips, demonstrating how structured prompting improves accuracy, creativity, and efficiency.

As Sabit Ekin (2023) states, “Prompt engineering is not just a convenience but a necessity in leveraging the full potential of LLMs.” This blog will unpack the findings, highlight real-world applications, and give you clear steps to improve your AI interactions.

The Consequences of Poor vs Good Prompting

A recurring theme across the literature is that poor prompts often generate irrelevant or misleading output, while refined prompts unlock LLMs’ deeper reasoning. For instance, Zheng et al. (2024) tested prompts in biomedical contexts and concluded that “vague queries led to incomplete or clinically unsafe answers, whereas carefully engineered prompts produced reliable, expert-level results.”

Consider this exaggerated business example:

• Poor Prompt: “Write me a marketing plan.”

• Good Prompt: “Create a 5-step digital marketing strategy for a mid-sized Irish e-commerce company, focusing on AI-driven customer retention and content automation. Keep it under 500 words.”

  
  

The first yields generic advice; the second produces a customised, structured roadmap. For content creators, a similar gap appears:

• Poor Prompt: “Give me a caption.”

• Good Prompt: “Write three playful Instagram captions under 20 words for a fitness brand launching a new protein shake.”

  
  

The difference is not trivial—it determines whether AI adds business value or wastes resources.

What the Research Says: Prompting as a Business Skill

Prompt Engineering Defined

Prompting is more than just typing requests. Durieux et al. (2024) explain: “The art of prompting lies in framing the task, constraining the answer, and providing the context necessary for the model to deliver accurate and useful output.” This makes prompting akin to design thinking—where clarity, scope, and iteration drive outcomes.

Businesses Need It Across Industries

From banking to manufacturing, prompts are transforming workflows. Ekin (2023) emphasises that, “the same model may behave like a novice or an expert depending on how the prompt is crafted.” For businesses, this means AI can serve as either a confused intern or a seasoned consultant—depending on user skill.

For healthcare, Zheng et al. (2024) highlight that prompting can guide models to “distinguish between possible diagnoses with nuance, provided the instructions specify context and required level of certainty.” For marketing, Slavych & Williams (2025) show how creative prompts generate targeted campaigns, maximising ROI when instructions specify tone, audience, and constraints.

Practical Examples from the Studies

Example 1: Breaking Down Complex Tasks

Durieux et al. (2024) recommend breaking prompts into smaller, manageable parts. Instead of asking “Write me a 20-page report,” a better sequence is:

1. “Generate a table of contents for a 20-page report on AI in logistics.”

2. “Write the introduction in under 300 words, citing three key trends.”

3. “Draft section one on predictive analytics in supply chains with practical case studies.”

   
   

This step-by-step approach mirrors agile project management and results in more coherent outputs.

Example 2: Embedding Context

Sabit et al. (2023) demonstrated in engineering contexts that prompts containing contextual details produced more reliable results. For instance, instead of “Explain reinforcement learning,” they used: “Explain reinforcement learning as if to senior managers in manufacturing, focusing on predictive maintenance of machines.” The latter delivered industry-specific, actionable insights.

Example 3: Iterative Refinement

Slavych & Williams (2025) emphasised iterative prompting: test → refine → re-prompt. They note, “Effective use of LLMs is not a one-shot interaction but a dialogue where the human guides and adjusts the machine’s trajectory.” Businesses should treat AI like a partner who improves with feedback rather than a tool that must get it right the first time.

Key Prompting Strategies for Businesses

1. Clarity and Specificity

• Poor: “Summarise this document.”

• Better: “Summarise this 10-page market report into five key bullet points highlighting risks and opportunities for SMEs.”

  
  

2. Format and Constraints

• Poor: “Write about fraud detection.”

• Better: “Produce a 300-word LinkedIn article on fraud detection in e-commerce. Use a professional but engaging tone.”

  
  

3. Role Assignment

Assign the model a persona. For example:

• “Act as a senior data analyst and explain the business risks of poor customer retention using AI insights.”

  
  

This technique, validated in Sabit et al. (2023), anchors responses in expertise.

4. Examples and Few-Shot Learning

Provide examples in the prompt. For content creators:

• “Here are two sample captions we like: [examples]. Now generate five more in the same style.”

  
  

This few-shot prompting reduces randomness and aligns results with brand tone.

5. Iteration and Dialogue

Treat AI like an evolving conversation. Start broad, refine, and then narrow. Businesses in case studies (Durieux et al., 2024) achieved up to 40% improvement in relevance by using this iterative method.

Implications for Content Creators

For creators, prompting is both art and science. The research highlights that clarity in tone, length, and audience is essential. A YouTube script request framed as:

• “Write a 5-minute script for a tech channel explaining AI in simple terms for teenagers, using humor and analogies.”

  
  

outperforms the vague alternative:

• “Write a YouTube script about AI.”

  
  

Creators can use prompts to automate content ideation, accelerate drafts, and refine brand style. But the power lies in how they ask, not just what they ask.

Conclusion: The Five Keys to Better Prompts

Drawing on the peer-reviewed studies, businesses and creators can improve prompting by focusing on five essentials:

• Be clear and specific in framing tasks.

• Provide context and role assignment for targeted answers.

• Break down complex tasks into smaller steps.

• Define tone, format, and constraints clearly.

• Use iteration and feedback to refine results.

  
  

As Ekin (2023) reminds us, “Prompt engineering is a bridge—transforming the vast potential of LLMs into meaningful, domain-specific applications.” Businesses that learn this skill will not only save time but also unlock competitive advantage in marketing, content, and decision-making.

References

Durieux, B., Archambault, P.M., Bellemare, C.A., Carrier, F.M., Gagnon, M.P., Gosselin, E. and Légaré, F., (2024). How to Get the Most Out of ChatGPT? Tips and Tricks on Prompting. European Journal of Cardiovascular Nursing, 23(1), pp.128–130.

Ekin, S., (2023). Prompt Engineering for ChatGPT. Texas A&M University, Department of Electrical and Computer Engineering.

Sabit, E., Kayaalp, M., & Tekin, M. (2023). Prompt Engineering in Biomedical Applications. Annals of Biomedical Engineering, 51, pp. 2113–2129.

Slavych, B., & Williams, G. (2025). Crafting Effective Prompts for ChatGPT: A Tutorial. Internet Journal of Allied Health Sciences and Practice, 23(2), pp.1–8.

Zheng, J., Li, X., & Wang, Y. (2024). Evaluating Prompting Strategies in Biomedical Large Language Models. AI & Society, 39, pp. 1123–1139.

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