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Master prompt engineering: Proven strategies and hands-on examples

April 15, 2024

In the not-so-distant future, generative AI is poised to become as essential as the internet itself. This groundbreaking technology vows to transform our society by automating complex tasks within seconds. It also raises the need for you to master prompt engineering. Let’s explore how.

Harnessing generative AI’s potential requires mastering the art of communication with it. Imagine it as a brilliant but clueless individual, waiting for your guidance to deliver astonishing results. This is where prompt engineering steps in as the need of the hour.


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Excited to explore some must-know prompting techniques and master prompt engineering? let’s dig in!


Pro-tip: If you want to pursue a career in prompt engineering, follow this comprehensive roadmap.

What makes prompt engineering critical?

First things first, what makes prompt engineering so important? What difference is it going to make?

The answer awaits:


Importance of prompt engineering
Importance of prompt engineering


How does prompt engineering work?

At the heart of AI’s prowess lies prompt engineering – the compass that steers models towards user-specific excellence. Without it, AI output remains a murky landscape.

There are different types of prompting techniques you can use:


7 types of techniques to master prompt engineering
7 types of prompting techniques to use


Let’s put your knowledge to test before we understand some principles for prompt engineering. Here’s a quick quiz for you to measure your understanding!


Let’s get a deeper outlook on different principles governing prompt engineering:


How generative AI and LLMs work


1. Be clear and specific

The clearer your prompts, the better the model’s results. Here’s how to achieve it.

  • Use delimiters: Delimiters, like square brackets […], angle brackets <…>, triple quotes “””, triple dashes —, and triple backticks “`, help define the structure and context of the desired output.
  • Separate text from the prompt: Clear separation between text and prompt enhances model comprehension. Here’s an example:


master prompt engineering


  • Ask for a structured output: Request answers in formats such as JSON, HTML, XML, etc.


master prompt engineering


2. Give the LLM time to think:

When facing a complex task, models often rush to conclusions. Here’s a better approach:

  • Specify the steps required to complete the task: Provide clear steps


master prompt engineering


  • Instruct the model to seek its own solution before reaching a conclusion: Sometimes, when you ask an LLM to verify if your solution is right or wrong, it simply presents a verdict that is not necessarily correct. To overcome this challenge, you can instruct the model to work out its own solution first.

3. Know the limitations of the model

While LLMs continue to improve, they have limitations. Exercise caution, especially with hypothetical scenarios. When you ask different generative AI models to provide information on hypothetical products or tools, they tend to do so as if they exist.

To illustrate this point, we asked Bard to provide information about a hypothetical toothpaste:


master prompt engineering



Read along to explore the two approaches used for prompting


4. Iterate, Iterate, Iterate

Rarely does a single prompt yield the desired results. Success lies in iterative refinement.

For step-by-step prompting techniques, watch this video tutorial.



The goal: To master prompt engineering


Explore a hands-on curriculum that helps you build custom LLM applications!


All in all, prompt engineering is the key to unlocking the full potential of generative AI. With the right guidance and techniques, you can harness this powerful technology to achieve remarkable results and shape the future of human-machine interaction.

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