Essential Generative AI Skills

GenAI is a type of artificial intelligence (AI) that can develop original content, such as videos, images, or text, in response to a user’s request. GenAI offers vast productivity benefits for improving workflows and enhancing products and services. The global genAI market was valued at $16.87 billion in 2024 and is likely to grow at a compound annual growth rate (CAGR) of 37.6 percent from 2025 to 2030 [1].

Discover more about genAI models, programming languages, output evaluation, and content creation, and learn how to build generative AI skills you can use in your own work environment or for personal use.

Core competencies in generative AI

GenAI utilizes methods like machine learning and deep learning to replicate the decision-making functions of the human brain, which means you may need to get a firm grasp on both to maximize the technology's use.

Understanding generative models

Deep learning and machine learning are two methods you can use to implement genAI. Deep learning relies on neural networks to simulate the human brain. It powers various applications that enhance automation, enabling the execution of analytical and physical tasks without human intervention. Machine learning (ML) makes it possible for computers and machines to “learn” much the way people do, implementing tasks autonomously and enhancing model performance through increased exposure to data. 

Familiarity with GANs and VAEs

To develop content autonomously, you can work with two deep learning models: generative adversarial networks (GANs) and variational autoencoders (VAEs). 

• VAEs: Implement natural language processing (NLP) detection, image recognition, and outlier detection

Autoencoders are deep learning models composed of two connected neural networks. One neural network encodes and compiles massive volumes of unstructured data, while the other decodes the data to rebuild the content. 

• GANs: Enhance the precision of past applications and authorize AI solutions for photorealistic image generation

GANs also utilize two neural networks to develop and evaluate new content. One neural network, known as a generator, generates new content, while the other, known as a discriminator, evaluates the accuracy and quality of the newly generated data. 

Knowledge of natural language generation

Natural language generation (NLG) is a component of genAI that enables machines and computers to write and speak in human language. NLG uses AI to take data and translate it into text or speech. NLG consists of text and sentence planning, and realization. Text planning implements the analysis of the content within the text. Sentence planning splits paragraphs into sentences to make it easier to comprehend. Realization ensures that the content produced is grammatically correct. The process of NLG ensures that the output is comprehensible and sounds human. 

Technical skills

Various technical skills, including familiarity with coding languages, can help when utilizing genAI to train AI models and take on opportunities and obstacles within GenAI. 

Programming proficiency

Python is a programming language that GenAI relies on for its projects. Python consists of various NLP libraries, including TensorFlow, PyTorch, and Jax. These libraries enable the implementation and deployment of genAI models. 

AI frameworks

PyTorch is a Python-based, open-source ML framework for building neural networks and accelerating GenAI models. It is one of the most widely used deep learning frameworks, known for its flexibility and computational capabilities. It’s user-friendly and utilized across various domains, including NLP and image classification. 

Creativity and innovation

GenAI creativity and innovation are essential skills for genAI projects since they help you develop unique content, clarify goals, and identify the limitations of your tasks. 

Developing unique content

GenAI has the ability to develop various types of unique content and designs, such as: 

• Images: GenAI models can generate unique images and edit pre-existing images. 

• Videos: GenAI video tools can develop animations based on the user’s request and apply special effects to videos quickly and efficiently. 

• Music: GenAI models can develop unique music that replicates the sound of professional musicians. 

• Simulations: GenAI utilizes existing data and content to create synthetic data and structures. 

Exploring new applications of generative AI

GenAI is emerging in various organizations, including marketing, business analytics, and communication. Industries are utilizing GenAI in their marketing strategies since it has vast capabilities in developing and refining data, content, and creative ad campaigns. Business analysts are leveraging GenAI to optimize data preparation, generate insights, and implement data-driven decisions. GenAI enhances communication within industries such as education to plan, write, and finalize text. 

Analytical skills

GenAI can help you implement accurate data preparation and make data-driven decisions by evaluating generated outputs. 

Evaluating generated outputs

Model output validation refers to the process of validating the accuracy of the output developed by the ML model. AI trainers can implement output validation by comparing the response to reliable existing data sets and information to ensure the model generates correct information. Output evaluation enables the models to identify mistakes, which builds trust and confidence in the AI model. 

Understanding limitations in genAI models

Although genAI has various benefits, it’s important to recognize the limitations that come with the generation of data and information. It may produce false information, known as “hallucinations.” GenAI hallucinations occur when models perceive nonexistent or incorrect information, developing inaccurate and incoherent outputs. Models can also share confidential information, which can pose security risks, especially in finance, health care, and legal advice. 

How to gain more generative AI skills

As GenAI evolves, various skills are necessary to thrive in the industry. ChatGPT is an emerging GenAI tool utilized for content creation within a wide range of organizations. Exploring ChatGPT and learning more about how to use it on the OpenAI website can offer practical knowledge as you build your generative AI skill set.

NLP and AI image processing are generative AI skills that benefit various companies. You can use NLP technology to enable computers to better comprehend human language and, in turn, enhance chatbots and AI assistants. AI image processing allows you to process and evaluate images and extract data from various documents. 

Excel in generative AI with Coursera

GenAI has various capabilities and limitations that should be considered before using its tools and models. Consider building your technical knowledge and generative AI skills by taking courses from leading institutions. For example, you can broaden your understanding of genAI with DeepLearning.AI’s Generative AI for Everyone or explore the benefits and drawbacks of genAI with the University of Michigan’s Generative AI Essentials: Overview and Impact. You can find these options and more on Coursera.