Skip to main content
HomeBlogArtificial Intelligence (AI)

How to Become a Prompt Engineer: A Comprehensive Guide

A step-by-step guide to becoming a prompt engineer: skills required, top courses to take, with career advancement tips.
Feb 2024  · 9 min read

Since the launch of ChatGPT in 2022, there’s a new buzz in the industry - Generative AI. It’s changing the way we interact with and think about artificial intelligence. With conversational AI such as ChatGPT and Google Gemini, you just need to type in a few prompts into the tool and the AI model usually responds exactly with the information you’ve asked for. Now, the question is, who are the professionals creating these exceptional AI models?

ML engineers, AI engineers, data scientists, and, obviously, prompt engineers are always researching and bringing incredible AI abilities to the table. So, starting a career as a prompt engineer could be the next big thing, just as being a software developer was back in the early 2000s.

Whether you’re already a data professional or AI is your passion, this guide is for you. Throughout this article, we’ll detail step-by-step instructions on how to become a prompt engineer. Let’s dive in!

What Does a Prompt Engineer Do?

Any non-tech individual today can go to ChatGPT and ask for something they want. Then, why do we need prompt engineers? Well, prompt engineers know how to ask the AI model to generate accurate outcomes. They craft the right questions and create triggering prompts to make the model generate the best results.

Imagine you need Python code to develop a login page, so you might ask GPT something like: “Write a Python code to create a login page,” but someone who knows prompting phrase it as: “Act like a Python developer and show a junior developer how to create a login page that will have input fields for username and password, along with a login button.” That's the essence of prompt engineering.

Prompt engineers also do a lot more technical aspects of dealing with large language models. They test and fine-tune AI models using prompts. The process involves tracking an AI model’s behavior by analyzing its responses to various prompts. Then, they look for patterns and biases, run A/B tests on the output data, and refine prompts to address limitations.

Overall, prompt engineers are the key players in optimizing the entire AI prompt generation process for custom applications.

How to Become a Prompt Engineer

image1.png

Becoming a prompt engineer is a smart career move, especially as Gen AI is one of the fastest-growing technologies, expecting a compounding annual growth rate of 20% for the next ten years. Here’s a deep dive on how to become an AI prompt engineer.

Learn the Fundamentals

1. Master Python

Prompt engineers would need a solid understanding of Python programming. Knowing how to code in Python allows you to learn NLP and deep learning models quickly.

As a prompt engineer, you may not build an entire language model by yourself. But some high-paying prompt engineering positions expect you to analyze the data generated by language models, and gain insights into it to make the model better. And when it comes to handling data, Python stands out.

Follow these steps to learn Python for prompt engineering:

  • Learn syntax
  • Explore NumPy, Pandas, Matplotlib, and Scikit-learn libraries
  • Get hands-on with NLTK, spaCy, TextBlob, and other natural language libraries
  • Practice data analysis
  • Learn about machine learning models
  • Practice Python coding

2. Understand the basics of AI

Begin your professional journey by familiarizing yourself with the technology you’re going to leverage. In this case, you should first understand what AI is and then explore its subfields. The basic idea behind artificial intelligence (AI) is quite fascinating - making machines behave intelligently and similarly to humans to perform specific tasks.

Machine learning, natural language processing, data science, deep learning, and many other subfields are part of this larger area of AI. First, have an overview of each of these technologies and know how they differ from each other.

Check out the resources below to learn AI fundamentals:

Large language models have a really strong relationship with deep learning and NLP. Although not all prompt engineers build an AI model from scratch, it is expected that they have strong NLP and deep learning skills to understand the language model they prompt.

3. Learn about NLP

Natural language processing is one of the hottest topics in AI at the moment. It lets computers read and interpret human languages. What’s even more is that it enables machines to generate human-like responses in natural language.

Plus, NLP helps you design triggering prompts that can shape an AI model to generate targeted outputs. So, as a prompt engineer, NLP is something you should master. Explore NLP libraries like NLTK, spaCy, and Transformers to get ready to handle language data.

You’ll want to start by mastering basic text processing and go deeper into techniques like tokenization, sentiment analysis, and text summarization.

Here are some of the best NLP related articles and courses:

4. Dive Deep into Deep Learning and Transformer Models

As you work to become a prompt engineer, you’ll often encounter large language models like GPT, Gemini, LLaMA 2, and more. These LLMs are nothing but scaled deep learning models designed to understand and generate natural language. To handle them effectively, you’ll need to have a solid grasp of deep learning concepts.

ChatGPT, for instance, is fed on over 175 billion neurons. To understand what these neurons are and how they are fed, you should first know the idea of neural networks, the backbone of many deep learning algorithms.

Next, you might know that the ‘T’ in GPT stands for ‘Transformer’. The popular large language models are built on the Transformer architecture, including ChatGPT.

Transformer architectures use what’s known as an “attention mechanism” – a technique to improve model accuracy by focusing only on the relevant parts of the input data. Studying these architectures will help you craft effective prompts.

Develop Practical Skills Development to Become a Prompt Engineer

While you don't necessarily need a degree to get into prompt engineering, you should have some practical experience in certain technical skills. Let’s explore the languages and techniques you need to excel in.

5. Hands-on with Pre-trained Models

Prompt engineers spend a considerable part of their time working with pre-trained models. In case you don’t know, pre-trained models are the base AI models that are extensively trained on tons of data. This is the very first step of building any large language model.

If you truly want to master prompt engineering, get familiar with existing pre-trained models such as GPT-2, GPT-3, BERT, and more. Input different prompts and study how they react. Understand their text generation capabilities and spot limitations.

Moreover, understand the parameters these models are trained on. At times, you’ll need to tune their hyperparameters to achieve specific results.

6. Fine-tuning for Custom Applications

Only a few companies can afford to train large language models from scratch. That’s why others bring you in as a prompt engineer, tasking you with fine-tuning the current pre-trained models for their custom applications.

For example, a pre-trained model may not be able to generate an HTML file according to your design requirements. But you can fine-tune it on UI and HTML-specific datasets to get desired outputs.

Overall, a prompt engineer should be able to fine-tune any pre-trained model for specific tasks. This can be done by feeding the pre-trained models with small and specific datasets.

Data preprocessing, hyperparameter tuning, and transfer learning are must-have skills to fine-tune a pre-trained model everything by yourself.

Remember, the depth of these technical skills varies based on the requirements of the position you’re applying for.

Usually, for entry-level roles, just NLP concepts and clever prompt crafting capabilities are enough to enter. However, a senior-level or high-paying gig might require more profound hands-on experience with NLP libraries, deep learning algorithms, and advanced prompting techniques.

Here are some of the comprehensive guides to help you learn fine-tuning:

Prompt Engineering Techniques

Explore different prompting techniques and learn to be creative with your writing. Let’s see how you can be better at creating prompts.

7. Master Prompt Crafting

Language models produce results based on the inputs you give. If you can explain an AI model exactly what you want and tune it to produce desired outputs, that's it; you can call yourself a prompt engineer.

Writing clear prompts and improving existing prompts are two of the main jobs of prompt engineers. They have to draft questions and sentences that can steer an AI model towards relevant responses.

For this, you have to be good at writing, and a tip to write better prompts is to include context and instructions in them. However, to become a master in prompt engineering, you’ll need to learn some advanced prompt engineering techniques, as we go through in the next section.

8. Advanced Prompting Techniques

We’ve already mentioned you to experiment with pre-trained models, and it’s a point that bears repeating. Use DALL-E, GPT-2, GPT-3, or BERT to practice different prompting techniques and see how they behave.

There are various strategies for model prompting such as zero-short prompting, one-shot prompting, iterative prompting, and many more. You can learn and practice these prompting techniques to become an exceptional prompt engineer.

To not miss out on anything, learn about LangChain. We’ve got a full guide covering LangChain for prompt engineers, but for a quick overview, LangChain is a framework for maximizing the accuracy and relevance of the model outputs.

Career Advancement and Real-world Application

While having a degree in a relevant computer science or engineering field is a plus, it isn’t the only route. You can become a prompt engineer without a degree by completing certification courses, working on real-world projects, and continuous learning. Let's delve into each step to shape your career in prompt engineering.

9. Real-world Projects and Portfolio Building

With a clear idea of AI technology and the skills above, you can start applying your new expertise to real-world projects. Whether you’re a working professional or a university student, volunteer for projects that need prompt engineering.

Here are some of the great AI projects you can start building:

If your project uses a pre-trained model, have a technical understanding of how it’s built and how it behaves. Try out different ways of asking the model to do things and see how it responds. Use the advanced prompting techniques you’ve learned to trigger the model. This way, you can turn your theory into skills.

As you go, you can join in conversational AI and LLM-related competitions on Platforms like Kaggle to see where you stand among your peers. Remember, everything you do in this step can go into your portfolio, making your profile even stronger.

Having a professional portfolio is crucial at any stage of your career. Datacamp has an easy editing portfolio builder with beautiful pre-made templates. So, leverage this free portfolio builder to showcase your skills, experience, projects, and interests in an attractive way.

10. Keep Learning & Engage with the AI Community

Take online courses; there are a lot of prompt engineering courses that have been added recently. Certification courses not only help you acquire skills but also add evidence to your profile that you actually learned the technology.

It’s necessary to stay current with the latest trends, especially in booming tech like OpenAI's Sora. There is no better way to stay updated than engaging with the right community. For this, follow top prompt engineering researchers and developers on sites like LinkedIn, Medium, and Twitter.

Conclusion

Now is the right time to get started with Gen AI. The world of tech companies is looking for prompt engineers more than ever to implement conversational AI in their businesses.

The path to becoming an AI prompt engineer is both rewarding and exciting. Anyone can become a prompt engineer with the right combination of the technical and non-technical skills mentioned throughout this article.

If you’re serious about your career in prompt engineering, explore the following courses from Datacamp:


Photo of Srujana Maddula
Author
Srujana Maddula

Srujana is a freelance tech writer with the four-year degree in Computer Science. Writing about various topics, including data science, cloud computing, development, programming, security, and many others comes naturally to her. She has a love for classic literature and exploring new destinations.

Topics

Start Your Prompting Journey Today!

Course

Understanding Prompt Engineering

1 hr
8.3K
Master ChatGPT for business & creativity: Learn prompt engineering, zero to few-shot learning, and response evaluation.
See DetailsRight Arrow
Start Course
See MoreRight Arrow
Related

You’re invited! Join us for Radar: AI Edition

Join us for two days of events sharing best practices from thought leaders in the AI space
DataCamp Team's photo

DataCamp Team

2 min

The Art of Prompt Engineering with Alex Banks, Founder and Educator, Sunday Signal

Alex and Adel cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and much more.
Adel Nehme's photo

Adel Nehme

44 min

The Future of Programming with Kyle Daigle, COO at GitHub

Adel and Kyle explore Kyle’s journey into development and AI, how he became the COO at GitHub, GitHub’s approach to AI, the impact of CoPilot on software development and much more.
Adel Nehme's photo

Adel Nehme

48 min

A Comprehensive Guide to Working with the Mistral Large Model

A detailed tutorial on the functionalities, comparisons, and practical applications of the Mistral Large Model.
Josep Ferrer's photo

Josep Ferrer

12 min

Serving an LLM Application as an API Endpoint using FastAPI in Python

Unlock the power of Large Language Models (LLMs) in your applications with our latest blog on "Serving LLM Application as an API Endpoint Using FastAPI in Python." LLMs like GPT, Claude, and LLaMA are revolutionizing chatbots, content creation, and many more use-cases. Discover how APIs act as crucial bridges, enabling seamless integration of sophisticated language understanding and generation features into your projects.
Moez Ali's photo

Moez Ali

How to Improve RAG Performance: 5 Key Techniques with Examples

Explore different approaches to enhance RAG systems: Chunking, Reranking, and Query Transformations.
Eugenia Anello's photo

Eugenia Anello

See MoreSee More