What is Generative AI? Definition & Areas of Application

Introduction: Why generative AI is so relevant right now

Generative artificial intelligence (generative AI) is everywhere. From text generators like ChatGPT to image models such as DALL·E and Stable Diffusion, we are seeing a technological shift that opens new possibilities for creativity, automation, and efficiency. Generative AI holds particular promise for software development and quality assurance (QA). But what exactly is behind it?

What is generative AI?

Definition and distinction

Generative AI is a subfield of artificial intelligence focused on creating new content — text, images, code, music, or whole designs. Unlike “classical” AI, which typically analyzes data and makes predictions, generative AI produces novel outputs based on patterns learned during training.
 

Difference from classical AI

Classical AI is mostly analytical: it recognises patterns, classifies data, or optimises processes. Generative AI is creative: it generates content that did not previously exist. Its outputs often appear human-like and are especially useful in tasks requiring complexity or creativity.

How it works in short

Generative AI relies on large datasets (training/data basis) to build models such as large language models (LLMs) or diffusion models. Using so-called prompts, it generates outputs – text, images, or code snippets – that are statistically likely and contextually consistent.

How does Generative AI work?

Training with large datasets

The foundation of generative AI is vast datasets used to train models. These datasets may include text, source code, images, or audio. The broader and higher-quality the dataset, the better the results.

Role of LLMs, diffusion models & more

● LLMs (Large Language Models): Models like GPT-4 generate text, code, or dialogues.
● Diffusion models: Used for image generation (e.g., Stable Diffusion).
● GANs (Generative Adversarial Networks): Primarily relevant in research and realistic image synthesis.

Prompting and output formats

A “prompt” is the input that triggers the AI to produce an output. Prompt engineering is becoming a core skill to generate targeted content. Outputs range from realistic images to complex software code and creative texts.
 

Application areas of Generative AI

In software development

● Code completion: Tools like GitHub Copilot support developers when writing code.
● Documentation & comments: Automatically generated explanations improve collaboration.

In der Qualitätssicherung (QA)

● Test case generation: Automatically creating test cases from requirements.
● Bug prediction: AI detects potential defect sources earlier.
● Regression test automation: Faster tests for frequent releases.
● Test data generation: Realistic and diverse datasets at the push of a button.

Other business applications

● Content creation: For documentation, marketing copy, or internal communication.
● Prototyping: Quick mockups and design ideas with generative image AI.
● Knowledge bases: Automated structuring and classification of information.

Opportunities & Challenges

Efficiency potential

● Automation of repetitive tasks
● Faster development cycles
● Better scalability while maintaining quality
 

Risks & bias

● Bias due to skewed training data
● Hallucinations (false outputs)
● Security & privacy concerns with sensitive data
 

Responsible use

Organizations must define guidelines for applying generative AI. Transparency, explainability, and human oversight are key principles.

Conclusion: Understanding and using Generative AI

Generative AI is more than a hype. It is already changing how we develop, test, and communicate software. For software development teams and QA leads, it provides real value – provided it is used skillfully and responsibly.