3 Expert Prompting Techniques to Get Better Results from ChatGPT and Google Gemini

By now, I’m sure you’ve heard of ChatGPT and other large language models (LLMs), but you may wonder how to get the most out of them. The key lies in mastering the art of prompt crafting. Here, I’ll introduce three powerful prompting techniques with examples to help you get better results!

1. Persona Pattern
The persona pattern involves assigning the LLM a specific role, which helps shape its responses. When you ask the model to act as a particular character, it adopts that mindset, making its responses more tailored and effective.
Example:
"You are a personal trainer who specializes in low-impact exercises for seniors. How would you create a weekly workout plan for someone with limited mobility?"
By defining the model’s persona, you get contextually relevant, role-specific advice.

2. Reflective Pattern
This pattern involves asking the model to reflect on its previous answer. It’s great for improving clarity or catching mistakes. It pushes the model to “think” about its response, leading to more thoughtful, refined answers.
Example:
"Given your last response, how would you refine or improve that advice for someone who has knee pain?"
The model reevaluates its response, often leading to more specific and targeted outputs.

3. Few-Shot Prompting
Few-shot prompting involves providing a few examples in the prompt, helping the model recognize the format or style you’re seeking. This is especially helpful for generating consistent outputs.
Example:
"Here are some motivational quotes: 'Believe in yourself,' 'Hard work pays off.' Now, generate three more quotes in a similar style."
By showing examples, you guide the model toward producing results in the desired format.

Incorporating these patterns into your prompts will help you unlock the full potential of LLMs, giving you smarter, more focused outputs tailored to your needs.

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