A Guide to Prompt Engineering

Posted By :Aadarsh Pathak |12th February 2024

What is Prompt Engineering?
Effective communication with AI relies heavily on crafting precise commands, known as prompts. Prompt engineering, therefore, entails the meticulous process of formulating inputs that dictate the desired output from an AI language model. The quality of these inputs directly influences the quality of the generated output. In essence, well-defined prompts yield accurate and valuable responses, whereas poorly constructed prompts may result in misleading or undesirable outcomes. As the adage goes, "With great power comes great responsibility," emphasizing the importance of thoughtful prompt construction.
Prompt engineering is a universal concept applicable across various AI applications, spanning from chatbots and content generators to language translation tools and virtual assistants. You might be curious about the mechanics behind AI response generation.

How do Language Models Work?
AI language models like GPT-4 harness the power of deep learning algorithms and natural language processing (NLP) to grasp the intricacies of human language. This capability is fueled by extensive training on vast datasets comprising articles, books, journals, and reports, among other sources. Through this training, these models refine their understanding of language, enabling them to effectively comprehend and respond to various tasks.
In the training process, language models are typically fine-tuned using either supervised or unsupervised learning methods, depending on the specific model. Once trained, these models excel at generating text based on provided prompts, a process known as language modeling. This foundational capability forms the basis for numerous AI language applications, facilitating tasks ranging from chatbots to content generation.

What are Prompt Categories?
Utilizing prompts is essential for effective communication with AI language models. To craft high-quality prompts, it's crucial to comprehend their various classifications, allowing for the creation of structured prompts tailored to achieve specific desired responses.

  1. Information-seeking prompts -  These prompts aim to acquire specific information, typically addressing questions starting with "What" or "How." Examples include inquiries such as "What are Kenya's top tourist attractions?" or "How should I prepare for a job interview?"
  2. Instruction-based prompts - These prompts direct the model to perform particular tasks, resembling commands given to virtual assistants like Siri or Alexa. Examples include requests like "Call mom" or "Play the latest episode of my favorite TV show."
  3. Context-providing prompts -  As the name suggests, these prompts offer context to help the AI better understand the user's needs. For instance, a prompt might detail a scenario, such as "I'm planning a child's party; what decoration ideas and activities would you suggest?"
  4. Comparative prompts-  These prompts facilitate comparing or evaluating different options to aid decision-making. Example: "What are the strengths and weaknesses of Option A versus Option B?"
  5. Opinion-seeking prompts - These prompts solicit the AI's opinion on a given topic. For instance, one might ask, "What are your thoughts on the implications of time travel?"
  6. Reflective prompts -  These prompts encourage introspection and self-understanding, often based on personal experiences or topics of interest. Users may need to provide some information before receiving a relevant response
  7. Role-based prompts - These prompts generate responses based on a specific role assigned to the user's request. This category is commonly used, shaping responses according to the designated role.

A successful strategy for this specific category involves employing the 5 Ws framework, which comprises:

  • Who - Specify the role you require the model to embody, such as a teacher, programmer, chef, etc.
  • What - Denotes the task you wish the model to perform.
  • When - Indicates the desired timeframe for completing a specific task.
  • Where - Represents the place or circumstances pertinent to a given prompt.
  • Why - Signifies the rationale, incentives, or objectives behind a particular prompt.

An example of a role-based prompt is:

As a coding tutor, your role is to create personalized study plans to help individuals learn how to code. Your responsibilities will include understanding the goals, time commitment, and preferred learning resources of each student, and using that information to develop a comprehensive study plan with clear timelines and links to relevant resources. You should be able to adapt your teaching style to meet the individual needs of each student and provide ongoing support and guidance throughout the learning process. Your ultimate goal will be to help each student develop the skills and knowledge they need to achieve their coding goals.

This prompt should also include what you intend to learn, the intended learning period, and your goal for learning. Remember that the more details you give, the more tailored results you will get.

NOTE: It's essential to exercise caution when relying solely on the model's response if you're unfamiliar with the topic at hand. It's advisable to verify information from multiple sources if you have doubts about the accuracy of the model's responses, as they may not always be accurate.

Another example of role-based with Images:-

Here I uploaded an image in our app after calling the API the chat-gpt result shows like this :-

And my prompt is :-

You are an expert Frontend and Javascript developer

You take a text for a web page to design from the user, and then build a multi-page website within a single set of <html></html> tags

use Tailwind, HTML and JS.

- Make sure the app is also mobile responsive and looks good on smaller screens.

- Pay close attention to background color, text color, font size, font family,

padding, margin, border, etc., and all css part

- Do not add comments in the code such as "<!-- Add other navigation links as needed -->" , <!-- rest of your HTML code --> and "<!-- ... other news items ... -->" in place of writing the full code. WRITE THE FULL CODE.

- Repeat elements as needed to match the requirement. For example, if there are 15 items, the code should have 15 items. DO NOT LEAVE comments like "<!-- Repeat for each news item -->" or bad things will happen.

- For images, use placeholder images from https://placehold.co and include a detailed description of the image in the alt text so that an image generation AI can generate the image later.

- For Placeholder images use relevant images from https://www.pexels.com/ make use of images should me relevant and correct sizes for the website

- The website should look very professional and attractive

In terms of libraries,

- Use this script to include Tailwind: <script src="https://cdn.tailwindcss.com"></script>

- You can use Google Fonts

- Font Awesome for icons: <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"></link>

In this prompt, I clarify the context and limitations and provide a  role to the chatgpt that will easily be understood by  the chatgpt and i get the releva


Principles of Effective Prompt Engineering:-

When crafting prompts, it's crucial to adhere to the following principles:-

  • Clarity – Clear communication is paramount. Ensure your prompt is precise and unambiguous, enabling the AI to provide accurate responses..
  • Provide context and examples –Furnish additional information to aid the AI's understanding of the prompt's purpose, thereby enhancing response accuracy.
  • Set limitations and constraints – Establish boundaries to guide the AI's operation, minimizing the risk of irrelevant or undesirable information.
  • Break down queries –Divide complex queries into manageable segments to facilitate the AI's comprehension and improve response quality.
  • Iterate and rephrase – If initial responses are unsatisfactory, consider refining your prompt and providing additional context for improved results.
  • Prioritize important info – Highlight essential details in the prompt to prompt the AI to focus on providing relevant responses.
  • Use multiple choice questions – When faced with multiple options, present various choices to streamline the process and save time.
  • Request step-by-step explanation – Structure prompts to elicit detailed, thorough responses by instructing the AI to break down complex topics into manageable steps.
  • Encourage critical thinking – Foster critical thinking in the AI's responses, particularly when seeking advice or guidance, to ensure realistic and logical outcomes.
  • Verify the accuracy of generated response –Always verify AI-generated responses for accuracy and currency, ensuring informed decision-making based on reliable information.

    Current challenges with AI responses:-

It's essential to acknowledge that AI-generated responses may not always be accurate. Awareness of this limitation is crucial, and corrective action may be necessary if errors occur.

Furthermore, AI tends to conform to provided information, even if incorrect, emphasizing the importance of being well-informed when interacting with AI systems. In cases of inaccurate responses, rephrasing prompts and providing additional context can help improve accuracy.


It's evident that AI technology will significantly shape our future, revolutionizing how we navigate our daily lives at work, home, and school.

To maximize its potential, we must master effective communication with these systems. This is where prompt engineering becomes crucial. By mastering the art of crafting precise prompts, we enhance the interaction between humans and machines, unlocking new possibilities for collaboration and efficiency.


About Author

Aadarsh Pathak

Aadarsh is an experienced Frontend Developer with expertise in ReactJS, HTML, CSS, JavaScript, and jQuery. With over a year of industry experience, he has contributed to various client and internal projects. One of his notable contributions includes the Reception Module Portal Development (KRB), which is a ReactJS project with a Microservices architecture, encompassing modules such as Reception, Billing, and Physician. Aadarsh's creative mindset and analytical skills enable him to think critically and adapt to new technologies. He is dedicated to staying up-to-date with industry trends through continuous learning and reading.

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