Open In App

What is Prompt Engineering – Meaning, Working, Techniques

Last Updated : 13 May, 2024
Like Article

Prompt engineering is rapidly emerging as a critical skill in the age of Artificial Intelligence (AI). As AI continues to revolutionize various fields, prompt engineering empowers us to extract the most value from these powerful models. This comprehensive guide dives deep into the world of prompt engineering, exploring its core principles, applications, and best practices.

Prompt Engineering

What is Prompt Engineering?

Prompt engineering is the process of creating effective prompts that enable AI models to generate responses based on given inputs. Prompt engineering essentially means writing prompts intelligently for text-based Artificial Intelligence tasks, more specifically, Natural Language Processing (NLP) tasks. In the case of such text-based tasks, these prompts help the user and the model generate a particular output as per the requirement. These requirements are efficiently added in the form of prompts and hence the name Prompt Engineering. 

What are Prompts?

Prompts are short pieces of text that are used to provide context and guidance to machine learning models. When talking about the specific text AI tasks, also called NLP tasks, these prompts are useful in generating relevant outputs which are as close to the expected output itself. Precisely, these prompts help in generating accurate responses by:

  • Adding on some additional guidance for the model.
  • Not generalizing a prompt too much.
  • Make sure the information added is not too much as that can confuse the model.
  • Making the user intent and purpose clear for the model to generate content in the relevant context only.

Prompt Engineering: Why is it Important?

  • More specific formats of input as prompts help in better interpretability of the requirements for a task.
  • Specific prompts with a detailed explanation of the requirements mean output matches more with the desired one.
  • Better results for NLP tasks, through prompts also mean a better-trained model for future tasks.

How Prompt Engineering Works?

Imagine you’re instructing a very talented but inexperienced assistant. You want them to complete a task effectively, so you need to provide clear instructions. Prompt engineering is similar – it’s about crafting the right instructions, called prompts, to get the desired results from a large language model (LLM).

Working of Prompt Engineering Involves:

  • Crafting the Prompt: You design a prompt that specifies what you want the LLM to do. This can be a question, a statement, or even an example. The wording, phrasing, and context you include all play a role in guiding the LLM’s response.
  • Understanding the LLM: Different prompts work better with different LLMs. Some techniques involve giving the LLM minimal instructions (zero-shot prompting), while others provide more context or examples (few-shot prompting).
  • Refining the Prompt: It’s often a trial-and-error process. You might need to tweak the prompt based on the LLM’s output to get the kind of response you’re looking for.

Applications of Prompt Engineering 

Essentially, the critical area where prompt generation is used the most is text-based modeling: NLP. As already stated above there are multiple ways in which prompt engineering can add more context, meaning as well as relevance to the prompts and hence generating better outputs.
Some of the critical applications of Prompt Generation are in the following tasks:

  • Language Translation: It is the process of translating a piece of text from one language to another using relevant language models. Relevant prompts carefully engineering with information like the required script, dialect, and other features of source and target text can help in better response from the model.
  • Question Answering Chatbots: A Q/A bot is one of the most popular NLP categories to work on these days. It is used by institutional websites, and shopping sites among many others. Prompts on which an AI chatbot Model is trained can largely affect the kind of response a bot generates. An example of what critical information one can add in a prompt can be adding the intent and context of the query so that the bot is not confused in generating relevant answers.
  • Text Generation: Such a task can have a multitude of applications and hence it again becomes critical to understand the exact dimension of the user’s query. The text is generated for what purpose can largely change the tone, vocabulary as well as formation of the text.

What are Prompt Engineering Techniques?

The purpose of the prompt engineering is not limited to the drafting of prompts. It is a playground that has all the tools to adjust your way of working with the big language models (LLMs) with specific purposes in mind.

Foundational Techniques

  • Information Retrieval: This entails the creation of prompts so that the LLM can get its knowledge base and give out what is relevant.
  • Context Amplification: Give supplementary context to the prompt in order to direct the understanding and attention of the LLM to its output.
  • Summarization: Induce the LLM to generalize or write summaries about complex themes.
  • Reframing: Rephrase your reminder to the LLM to consider a specific style or format for the output.
  • Iterative Prompting: Break down the complex tasks into smaller parts and then instruct the LLM sequentially in how to achieve the end result.

Advanced Techniques

  • Least to Most Prompting: First, begin with prompts of general nature and then add facts to drive the LLM to make a highly specialized solution for intricate problems.
  • Chain-of-Thought Prompting: Require the LLM to show the steps of its reasoning as well as the answer, leading to enlightenments for our understanding of its thinking.
  • Self-Ask Prompting: This thus entails chaining-of-thought prompting, which involved the LLM being prompted to ask itself clarifying questions to get to a solution.
  • Meta-Prompting: This experimental method investigates designing a single, common prompt that can be used for diverse tasks by way of additional instructions.

Prompt Engineering: Best Practices

Prompt engineering is a crucial task with multiple aspects and features to be balanced with precision. The performance of a model can largely be tuned by using a correct prompt. The question arises how can we make sure that our prompt is right for the task at hand?
Following are some key points one should remember while engineering a prompt for an AI model:

  • Begin with Objectives and Goals: AI models often work based on manual input only. The raw input used to train a model or the running text or conversation one does with a language model all affect how the model will reply to the next query. Hence, whenever interacting with a model, the goal of the conversation and the objectives to be achieved via it should be absolutely clear even before one begins. 
  • Relevant and Specific Data Identification and Usage: As clearly stated just like every prompt and its objective should be described clearly, similarly, only absolutely relevant data should be used to train a model. One should make sure there is no irrelevant or unnecessary data in the training.
  • Focus on finding the Relevant Keywords: Relevant keywords, make a huge difference in the type of response generated. A keyword used correctly in the right place can lead to a much different result altogether. For example, working on a mathematical problem, if someone asks a model: “What are planes?”, there can be a very high chance if the keyword “mathematics” is not mentioned, the AI will consider the word plane in the context of airplanes. Hence, keywords should be used wherever necessary! 
  • Make sure your prompts are simple and clear: When crafting prompts, it’s important to keep them simple and clear by using plain language and avoiding overly complex sentence structures. This will make it easier for the model to generate accurate outputs and for users to understand the prompts.
  • Test and Refine Your Prompts: The final step is to use a variety of test cases to evaluate the performance of the generated prompts and make adjustments as needed. Further refining the prompts based on the tests will improve the accuracy of the outputs generated by your model.

By following the above best practices, you can create prompts that are tailored to your specific objectives and generate accurate and useful outputs.

Advantages and Disadvantages of Prompt Engineering


  • Improved accuracy: A relevant prompt, means better work by the AI model which in turn only means a refined response simulated for the situation with precision. It can also be considered very useful especially talking about the niche domains like healthcare.
  • Enhanced user experience: A better response only means a satisfied user who can easily get a response relevant to their problem without much of a hassle.
  • Cost-effective: The number of rounds needed to achieve a single accurate and satisfactory response reduces with one specific and neatly engineered prompt.


Difficulty in determining specificity: Determining the right balance between specificity and generality can be challenging, as a prompt that is too specific may limit the range of responses generated, while a prompt that is too general may produce irrelevant responses.

Future of Prompt Engineering

Prompt engineering is a very recently developing and upcoming technology and hence it can actually serve to be a very crucial part of most of the AI and NLP tasks and other areas as well. Here are some of the key areas where prompt engineering can actually help make great progress:

  1. AI and NLP: As AI and NLP technologies advance, one expects to see significant improvements in the accuracy and effectiveness of prompts. With more sophisticated algorithms and machine learning models, prompts will advance and be more particular to the specific use cases. 
  2. Integration with Other Technologies: Prompt engineering is likely to become increasingly integrated with other technologies, such as virtual assistants, chatbots, and voice-enabled devices. This will enable users to interact with technology more seamlessly and effectively, improving the overall user experience.
  3. Increased Automation and Efficiency: We can also expect to see increased automation and efficiency in the process along with more advanced prompts, hence, streamlining the development of prompts, therefore improving outputs.

FAQs – What is Prompt Engineering?

Q1. What is prompt engineering?

Prompt engineering is the process of creating effective prompts that enable AI models to generate responses based on given inputs.

Q2. Why is prompt engineering important?

More specific formats of input as prompts help in better interpretability of the requirements for a task. Specific prompts with a detailed explanation of the requirements mean output matches more with the desired one. Better results for NLP tasks, through prompts also essentially means a better-trained model for future tasks.

Q3. What are the challenges of prompt engineering?

A major challenge turns out to be striking a perfect balance in the prompts. This means the prompt should be general enough not to produce irrelevant prompts and specific enough to solve the purpose. 

Q4. What are the Advantages of prompt engineering?

The biggest advantage of prompt engineering is essentially similar to its importance, and that is, better prompts with clear requirements mean better outputs and desired results. 

Q5. What are the ethical concerns associated with prompt engineering?

The ethical concerns associated with prompt engineering include the potential for bias in the prompts used to train AI models and the potential for malicious use of AI models, such as deep fake videos or fake news.

Previous Article
Next Article

Similar Reads

Level up your ChatGPT Game with OpenAI's Free Course on Prompt Engineering for Developers
Want to learn Prompt Engineering? OpenAI made it possible for you! The parent company of ChatGPT launched a free ChatGPT course on prompt engineering for developers.[caption width="800"] [/caption]An interesting new course on prompt engineering called "ChatGPT Prompt Engineering for Developers" has been curated by renowned artificial intelligence (
3 min read
Prompt Engineering for Summarization
Nowadays one of the hot topics that has gained everyone's attention towards it is ChatGPT which has been launched by OpenAI. The web service is suspected to reduce many jobs as well as increase the efficiency of the people manifold as it can give tailored answers to your questions. Now if you have tried to explore a little bit about ChatGPT regardi
9 min read
Prompt Engineering for ChatBot
ChatBot helps save a lot of human resources and in turn money for the organization. But the usage of chatbots is very challenging because no such smart assistants were available which can help solve the diverse problems which are faced by people all over the world. But with the current introduction to LLM by the community has shown the possibility
6 min read
Prompt Engineering for Transformation of Text
In the series of learning "How to write better prompts and customize them as per the specific use?" so, that we do not need to read articles that say 30 prompts to make your life easy with ChatGPT:). In this article, we will see how can we use ChatGPT to transform a piece of text and use the LLM as a ChatBot. But before that, we need to set the API
6 min read
Prompt Engineering for Inference
You must have faced such questions in your exam when you are supposed to answer some questions based on the text or passage provided. This is also known as the process of inferring relevant information from a large piece of text. ChatGPT model is also efficient in performing such tasks we just need to provide clear instructions to the model. Import
6 min read
Hottest Job of 2023 with 2Cr Salary - AI Prompt Engineering
Are AI tools like ChatGPT & GPT-4 threatening your job security? Don't worry you're not alone in this black hole because, in the coming years, a report by Goldman Sach predicted that 300 million jobs will be diminished because of Artificial Intelligence taking over human jobs. And this is actually something to worry about. But we've also found
8 min read
What is ChatGPT Prompt Engineering Principles
With ChatGPT taking Content Writing and everything around it by storm, it is increasingly becoming important to also understand how to put it to best use. This is where ChatGPT Prompt Engineering comes into the picture. Prompt Engineering is the process of creating, clear, concise and easily understandable prompts that help the machine or the AI mo
8 min read
What is an AI Prompt Engineering?
AI Prompt Engineering is a specific area of artificial intelligence (AI) that focuses on developing and improving prompts to enable efficient communication with AI models. AI prompts play a crucial role in serving as a connection between machine comprehension and human objectives. These cues or prompts help AI systems understand information and pro
9 min read
7 Code Refactoring Techniques in Software Engineering
Being a developer how do you start working on a new project…?? Firstly you gather some basic requirements and then based on the requirement you start implementing the feature one by one. As you progress with your project and learn more about it, you keep adding and changing the code in your codebase. Later you also change the code to fix the bug an
9 min read
Data Anonymization - Definition, Meaning, Techniques
With extensive data collection, protecting individual privacy while harnessing the power of data for analytics has become a paramount concern. In this article we will explore the process of Data Anonymization, which serves as a vital solution to strike this delicate balance. [caption width="800"]What is Data Anonymization[/caption]Table of Content
7 min read