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Basic Tenets of Prompt Engineering in Generative AI

by Pranisha Rai
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Prompt engineering is a crucial aspect of unlocking the full potential of generative AI models like ChatGPT and instructing them to work effectively. A well-defined and effective prompt can guide the AI system to respond correctly, in the right direction, and with the right intent. A prompt can be a question, statement, command, complex instruction sentence, or phrase. Today, in this article, we will explore the basic tenets of prompt engineering in generative AI. 

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Prompt Engineering: A Communication Bridge

Prompt Engineering

Prompt engineering acts as the communication bridge between users and the AI model. It focuses on formulating and optimizing queries based on user input to get the desired outcomes from the language model. All AI models are different, and their responses to the same prompt will also be different. It requires domain understanding to incorporate the goal into the prompt. Generating prompts at scale requires problem-solving skills, a systematic approach, and strategy. This is where a prompt engineering role becomes crucial in understanding the strengths and weaknesses of AI models, analyzing the results, and adjusting strategies to generate relevant responses from them. Therefore, with the right and effective prompts, the AI model can generate responses with quality, relevance, and coherence. 

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Prompt: A Key to Unlocking the Potential of Generative AI

A prompt

To put it simply, a prompt is a language to communicate with all AI models with precision and clarity. A well-written prompt can help Gen AI models like ChatGPT understand and generate relevant and accurate responses in a straight-forward way. A well-written prompt is the perfect formula to use a Gen AI model like ChatGPT successfully.  

Examples of Effective Prompts and Poorly Written Prompts: 

To understand the difference between writing effective prompts and poorly written prompts, let’s have a closer look at them: 

  • Example 1: Subject-Related Question 
  1. Effective Prompt: Can you explain the impact of global warming and its effect on climate change? 
  1. Poorly Written Prompts: Tell me about the climate. 

An effective prompt’s question is clear and specific, which allows a generative AI model like ChatGPT to produce a relevant response. The bad prompt’s lack of clear direction leads to a generic and irrelevant response. 

  • Example 2: Framing a Context Question 
  1. Effective Prompt: What is the main goal of Professor Brand in the movie Interstellar? 
  1. Poorly written prompts: What happens in Interstellar? 

Here we can see an effective prompt that includes a movie name with the character name in the question. This enables AI to generate an accurate response, whereas a bad prompt lacks precision. Therefore, the output will have no relevance, and the answer will be general. 

  • Example 3: A Neutral and Unbiased Question 
  1. Effective Prompt: What are the benefits and drawbacks of using AI in healthcare? 
  1. Poorly written prompts: AI is great, right? 

In this last example, we can see that the well-written prompt contains thoughtful questions with both positive and negative aspects of AI in the healthcare industry. This is how we can command ChatGPT to form an unbiased answer. Using bad prompt AI will create biased answers, only including the positive aspects of AI and preventing the negative aspects of it. 

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What Are the Types of Prompts?

There are three types of prompts: text prompts, multimodal prompts, and custom prompts. Let’s have a look at them down below: 

  • Text Prompts: Out of three prompts, text prompts are the most common type of prompt. It includes written and spoken instructions that enable AI to produce outputs as answers to questions and complete sentences. Text prompts are used in natural language processing (NLP) tasks. 
  • Custom Prompts: As the name suggests, custom prompts provide the flexibility to fine-tune your prompts to enable AI to carry out specific tasks. It can be a simple sentence, or a multi-step query used for complex tasks. 
  • Multimodal Prompts: This type of prompt is different from custom and text prompts. In multimodal prompts, users can combine text with images and audio. Using these prompts, AI models can help with tasks like image captioning and generating diverse content. 
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Tips to Structure an Effective Prompt

Prompts are the direct way to communicate with generative AI. Poorly written prompts can lead to irrelevant, ambiguous, or even incorrect outputs. Therefore, it is always best to invest time, and crafting efficient prompts is crucial for getting relevance and precision. The following are some tips for writing an effective prompt: 

  • Clear and Concise: A well-crafted prompt should have context with important information and specific details that help the AI understand the background of the task. Elaborate on the main request with the correct style and tone. This way, you can prevent confusion and boost the efficiency of the response. 
  • Balance between Precision and Clarity: When crafting a prompt, you must be specific and tailor the sentence. Do not provide the details that are under AI limitations. With the perfect balance between precision and clarity, AI can generate relevant and insightful text. 
  • Continuous Experimentation: You can try writing different prompts for different AI models and figure out the right combination of prompts that works best for each AI model and provides the best results. 
  • Refine and Adjust Prompts: Prompt engineering is an ongoing process based on model performance and user feedback. Prompts need to be refined and adjusted so that the AI model can improve and remain relevant over time. 
  • Image Creation Prompts: While generating images, you must describe visual parameters such as the subject with a clear description, style-specific genre, and color palette. Because the image depends on the descriptive clarity of the prompt, a perfect example of an image prompt is as follows: 
  • Subject: A landscape with rugged hills and bright sunlight 
  • Style: Influence of Leonardo da Vinci paintings 
  • Color palette: green, yellows, and deep blues
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Basic Tenets of Prompt Engineering in Generative AI

Zero-Shots Prompt: 

It is a technique where an AI model can provide an answer without relying on training data. In this technique, judgment is based on the large language model (LLM). While most of the output of the zero-shot prompt is qualitative, LLM is neither trained nor retrained. Thus, a user asks the AI model correctly to classify a paragraph or summarize it. However, the AI model probably cannot classify a paragraph of X or Y as it does not understand the meanings of “X” and “Y.” But that being said, AI can understand the positive and negative; therefore, it can classify positive and negative sentiments. This method works since the AI model is familiar with these words during its training, so it can easily follow simple instructions.

An example of zero-shot prompting: Translate the following English text to Spanish: ‘Hi, how are you?’. 

Few-Shots Prompt: 

Similar to zero shots, the few-shot prompt also leverages the LLMs to perform specific tasks. In-context learning serves as a guide for the model to perform better with the demonstrations within the prompt. Also, it takes a minimal number of examples, known as shots. Users can generate the outputs either by code, image, or text. Without a doubt, LLMS performs well with the zero-shot prompt technique; however, it struggles with complex tasks. Its effectiveness varies based on the number of prompts provided.

Examples of generating the few shots prompt: 

What is the capital of India?’ Answer: ‘Delhi’.

Example 2:

Question: ‘Who is Mahatma Gandhi?’

Answer: ‘Father of the Nation’.

Now, create a question based on Indian history for a student who excels in geopolitics.” 

Chain-of-Thought (CoT) Prompt: 

This prompting technique falls under the category of advanced methods. Its main goal is to solve a problem sequentially. It first divides the complex task into small chunks and then connects sub-tasks logically. Once done, a series of prompts guide the LLM in a step-by-step manner. It improves the AI model with complex reasoning. Rather than focusing on other issues, it pays attention to the logical steps. This, in turn, boosts the accuracy and relevance of AI outputs. Due to this reason, the CoT prompt is extremely beneficial for education. Moreover, its performance also scales up with the increasing number of parameters. Also, when combined with few-shot prompting, AI models can not only understand but can also provide the reasoning behind it.

Examples of CoT prompts: Ansel has 50 apples. He buys five more apples, each containing five. Find the total number of apples Joe has now and provide a chain of thought for your reasoning.” Explain the process of climate change.

Role Prompt: 

It is a method where the AI model is provided with a specific role and followed by a question that the AI is expected to answer within that role. This prompting technique is considered a powerful strategy for shaping the output of generative AI models. With this, a user can control the style and tone and then fine-tune the model for particular tasks. Role prompting is beneficial for understanding the context at a deeper level. The user can simply assign the role to the AI and provide any context that they’re having difficulty understanding.

For instance, if you’re struggling with any medical question, you can assign the role of a doctor to AI, and it will generate a response that aligns with that medical topic.

Example of a role prompt: As a doctor, how would you explain the problem of obesity and how to prevent it?

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In the complex world of AI, mastering prompt engineering is crucial to leveraging generative AI models to their full potential. With the rise of generative AI, prompt engineering is one of the most trending topics and in-demand skills. If one masters the art of writing a quality prompt with precision, it will open doors for promising careers with a lucrative salary. Some examples of the basic tenets of prompt engineering in generative AI are as follows:  

  • To quickly save time and ensure consistency in your prompt design, create a folder and keep all the prompts that are used for common tasks. 
  • Try to collaborate with AI developers and domain experts. Understanding their technical knowledge and having domain expertise can help you write well-designed and effective prompts. 
  • Continuously monitor AI model performance and incorporate user feedback. Keep refining the prompts and fine-tuning models to ensure the utmost performance of AI models. 
If you are a prompt engineer looking for lucrative job opportunities with top companies, then sign up with Olibr now! 


First, you need to define and describe your inputs clearly and give directions; don’t provide generic requests. Secondly, if you want the output in a list or summary, then specify the format. Thirdly, you can provide AI with the examples in the prompts. Fourth, identifying error and fine-tune prompts over time. Finally, identify errors and fine-tune prompts over time. Finally, split the complex prompt into multiple prompts and make a series of steps, then use them together. 

Prompt engineering as a field is gaining a lot of traction, and with the rise of AI models, the future of prompt engineering looks promising with best compensation. 

To excel in this role, you need expertise in AI, ML, and NLP, along with strong communication skills essential for crafting effective prompts and asking insightful questions.

Prompt engineering in AI refers to the process of crafting effective prompts or instructions given to AI models to generate desired outputs or responses. It involves designing input queries or commands in a way that guides the model to produce the desired outcomes accurately and efficiently. Prompt engineering is crucial in fine-tuning AI systems, especially in language models like GPT (Generative Pre-trained Transformer), to generate specific types of content or perform particular tasks. By carefully designing prompts, developers can steer AI models towards producing more relevant and contextually appropriate outputs, enhancing their overall performance and usability for various applications.

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