Writing Better AI Prompts Starts With Thinking Clearly

TLDR: Good results from AI don’t come from “hacking” prompts — they come from knowing how to ask the right way. This post breaks down five prompting techniques that help non-technical users guide AI tools like Claude and GPT. You’ll learn what works, how to refine, and how to avoid the most common mistakes that waste time and return fluff.

Still Getting Generic AI Output? 

Most people think AI is underperforming. It’s not. You’re just not briefing it properly.

If you ask vague questions, you get vague answers. If you give no structure, the AI makes it up. But the good news: once you learn to frame a task clearly, AI becomes far more useful.

Prompt design isn’t a technical skill. It’s a thinking skill. And with a few techniques, you can turn average results into real outputs you can use.

Context First: What the Model Needs to Know to Help You

AI doesn’t know who you are, what your role is, or what tone you need unless you tell it.  That’s where contextual priming comes in.

Instead of asking:

“What strategies should we use to grow market share?”

Try:

“We’re a mid-sized B2B services firm. Competitors have started undercutting us with self-service tools. What strategies should we use to protect revenue and retain clients?”

Now you’re guiding the model. You’re giving it your world to work in. This one change often improves output dramatically. 

Refine, Don’t Restart: Prompting Is an Iterative Process

Very few good outputs happen on the first try. And they don’t have to.

Here’s how to improve fast:

• Review the response — what’s off? What’s close?
  
• Adjust your prompt — clarify, simplify, or focus the task
  
• Resubmit — don’t retype from scratch
  
• Ask follow-ups — request revisions on just one section or point
  

This is how pro users work with AI, like editing a junior colleague, not restarting from zero every time.

Five Prompting Techniques That Make a Real Difference

Chain of Thought Prompting

This technique breaks down complex problems into smaller, sequential steps, guiding the AI model through a logical progression. The result is a more coherent and comprehensive response.

Example:

For a mid-life fitness guide, instead of asking, “What should somebody in their fifties do to improve flexibility and balance?”, use structured steps:

Describe common problems related to flexibility and balance for people in their fifties.
Outline potential solutions.
Evaluate the pros and cons of each solution.

This approach encourages detailed and well-rounded answers, moving beyond basic lists to comprehensive guidance.

Few-Shot Prompting

Few-shot prompting involves providing examples within the prompt to help the model understand the desired format and output.

Example:

For a meeting summary:

Basic prompt: “Summarise the meeting based on the transcript.”
Few-shot prompt: *“Here are two examples of meeting summaries:
The team discussed project timelines and assigned tasks.
Key points included budget adjustments and resource allocation.
Now summarise this meeting based on the uploaded transcript.”*

The examples act as a guide, improving the AI’s ability to produce accurate and relevant summaries.

Meta Prompting

Meta prompting uses a two-step process where the AI first generates a secondary, more specific prompt, which is then used to create the final output. This enhances accuracy and contextual understanding.

Example:
Instead of directly asking, “Provide a travel guide for London,” a meta prompt could start with:

“What’s a popular travel destination in Europe?”
Once “London” is identified, the AI creates a more targeted travel guide.

This dynamic process ensures the AI focuses on the most relevant context before delivering the output.

Contextual Priming

Adding relevant background information to a prompt helps the model generate responses better aligned with specific needs.

Example:

OK: “What strategies should our company adopt to maintain its market share?”
Better: “Given recent market trends and increased competition, what strategies should our company adopt to maintain its market share?”

The additional context sets the stage for a tailored and insightful response.

ReAct (Reasoning and Acting)

ReAct combines reasoning and action steps in the prompt, encouraging the model to explain its answers while providing actionable recommendations.

Example:

For sustainable energy solutions:

“Consider environmental impact and cost efficiency. Recommend a solution and explain why it’s the best option.” This structured approach ensures the response is both actionable and well-justified.

Why This Matters for Business 

Most people still think prompting is a trick — or that it’s for engineers.

It’s not. Prompting is now a business skill. If your team knows how to:

• Frame problems clearly
  
• Guide tone and structure
  
• Revise iteratively

...they’ll get more from every tool they use. ChatGPT, Claude, Gemini: all of them.

That’s exactly what we teach in our AI Fundamentals Masterclass. No fluff. Just smart use of tools you already have taught with real business tasks in mind.

FAQ

Q: Do I need to learn prompt frameworks like a script?
A: No. Just learn how to give the model context, structure, and a clear goal. That’s 80% of it.

Q: What’s the difference between a good and bad prompt?
A: A good prompt includes who you are, what you want, and how it should be delivered. A bad prompt leaves the model guessing.

Q: Can I use these techniques with any AI model?
A: Yes. These work across Claude, GPT-4, Gemini, and others — though output quality still depends on the model.

Q: Isn’t this just basic communication?
A: Exactly. Prompting is structured communication with software. The better you brief, the better the output.

Q: Where can I learn how to do this properly?
A: The AI Fundamentals Bootcamp gives you hands-on practice using real prompts in real workflows — built for non-technical professionals.