The Learning with AI Blog

Vague Prompts Lead to Generic or Off-Topic Responses

AI needs clear direction to generate useful responses. If your prompts are too vague, you’ll get broad, unhelpful answers that lack depth.

For example, a L&D manager might ask:
❌ “Explain leadership.”

This could result in a random mix of definitions, historical perspectives, and leadership theories—none of which may be useful for workplace training. Instead, refine the prompt:
✅ “Explain transformational leadership and how it can improve employee engagement in hybrid teams.”

Why this works:

• It defines the leadership style you want to focus on.
• It includes a workplace scenario, ensuring the response is relevant.
• It targets a specific learning objective, making the AI’s response more practical.

The Fix: Be precise about what you need. Define the topic, add context, and focus on a clear goal.

Too Many Requests in One Prompt Overloads AI

This is called Singe-shot prompting and works best for short simple requests. Cramming multiple requests into a single prompt confuses AI, leading to fragmented or shallow responses.

For example, an HR trainer might ask:
❌ “Explain workplace diversity, its benefits, and how to address unconscious bias, and create a short paragraph introducing the topic to an audience of HR professionals, including translations to Japanese and Korean”

Most AI’s will touch on everything but in a rushed, surface-level way. Instead, break it down:
✅ “Define workplace diversity and its key components.”
✅ “List three benefits of workplace inclusion for business success.”
✅ “What strategies can organizations use to reduce unconscious bias in hiring?”

✅ “Combine these into an introductory paragraph suitable for an audience of HR professionals”

✅ “Translate this paragraph into Japanese and Korean”

Why this works:

• AI can focus on one concept at a time, delivering deeper, more structured answers.
• It prevents overloaded responses that lack focus.

The Fix: Break large topics into bite-sized, focused prompts. You’ll get better responses with more useful details.

Using Only One AI Model Limits Perspective

Not all AI models think alike. Each has different strengths, and sticking to just one means missing out on valuable insights.

For example, if you’re creating training on data privacy laws, comparing models might give:
✅ “What are the ethical risks of using AI in hiring?” → ChatGPT (broad overview)
✅ “What case studies exist on AI bias in recruitment?” → Claude (deeper analysis)
✅ “How do recent regulations address AI discrimination?” → Gemini (data-driven insights)

Why this works:

• ChatGPT is great for summaries and structured content.
• Claude tends to be better at long-form reasoning and nuanced discussions.
• Gemini can provide fact-based insights and data-backed responses.
• Microsoft CoPilot is great at leveraging the content of your emails and sharepoint sites.

The Fix: Cross-check AI responses across multiple models. This improves accuracy and gives a well-rounded view before finalizing training content.

Allowing AI to Constrain Your Thinking Stifles Creativity

If you always follow AI’s first suggestion, you risk producing predictable, uninspired training content.

Imagine Wes Anderson’s iconic film style: perfectly framed shots, pastel colours, and quirky symmetry. Now imagine if he suddenly changed everything, like when Bob Dylan went electric. If we allow AI to dictate creative choices, we lose the chance to break patterns and innovate.

For example, instead of blindly accepting AI’s first idea, challenge it:
✅ “What are unconventional ways to present leadership training?”
✅ “Suggest three alternatives to PowerPoint for interactive corporate learning.”

Then take these ideas to a colleague or your team for discussion.

Why this works:

• AI tends to repeat safe, common ideas. Pushing back helps discover more creative solutions.
• If AI suggests a standard format, asking for alternatives helps break the pattern.
• It is easy to forget that AI, while really good at many things, is not human. Human creativity still trumps AI. For now.

The Fix: Use AI as a brainstorming tool – not a creative ceiling. Push for unexpected ideas, challenge its suggestions and leverage your human colleagues.

Relying on AI for Human-Centric Tasks Misses Key Nuances

AI can summarise and analyse, but it can’t replicate human intuition, emotional intelligence, or complex judgment. Using AI to respond to your wife’s message about your child, or send a romantic message defeats the purpose of both; human connection.

For example, a team leader might ask AI:
❌ “Write a performance review for an underperforming employee.”

AI will generate a generic, robotic script that lacks empathy or personalised insights. Instead, use AI as a guide, not a replacement:
✅ “List best practices for giving constructive feedback to employees struggling with performance.”

Why this works:

• AI suggests frameworks and techniques, but you add the human touch.
• AI can help structure difficult conversations, but real dialogue needs human judgment.

The Fix: Use AI for support, not substitution. It should enhance human interactions, not replace them.