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Generative AI (GenAI)

Generative AI (GenAI)

Generative Artificial Intelligence (GenAI) describes a subfield of AI technologies focused on creating new content. A well-known framework introduced by Goodfellow et al. (2014)1 is the Generative Adversarial Network (GAN). It consists of two competing networks: a generator that creates realistic objects and a discriminator that evaluates them. The process continues until the discriminator can no longer clearly distinguish generated objects from real data.1 2 The figure below shows how a GAN works based on Google (2022).3

Schematic illustration of a Generative Adversarial Network
Schematic illustration of a Generative Adversarial Network

Notable text-to-image examples of generative models include DALL·E by Ramesh et al. (2021)4 and Imagen by Google5, which convert natural language descriptions into highly detailed, context-appropriate images. GenAI is not limited to text or image generation, but also includes the generation of source code. Machine learning plays a crucial role here, especially in automated code generation by models based on Transformer architectures - in particular GPT-like models - trained on massive code databases.6 7 8

In addition to generating new content, finding existing knowledge is also an important AI area. Therefore, the next section takes a deeper look at Retrieval-Augmented Generation (RAG).

  1. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks (No. arXiv:1406.2661). arXiv. https://doi.org/10.48550/arXiv.1406.2661 ↩︎ ↩︎

  2. Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive Growing of GANs for Improved Quality, Stability, and Variation (No. arXiv:1710.10196). arXiv. https://doi.org/10.48550/arXiv.1710.10196 ↩︎

  3. Google. (2022, Juli 18). Overview of GAN Structure | Machine Learning. Google for Developers. https://developers.google.com/machine-learning/gan/gan_structure ↩︎

  4. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-Shot Text-to-Image Generation (No. arXiv:2102.12092). arXiv. https://doi.org/10.48550/arXiv.2102.12092 ↩︎

  5. Google. (2025, Februar 5). Imagen 3. Google DeepMind. https://deepmind.google/technologies/imagen-3/ ↩︎

  6. Wermelinger, M. (2023). Using GitHub Copilot to Solve Simple Programming Problems. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 172–178. https://doi.org/10.1145/3545945.3569830 ↩︎

  7. Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., Vasilakos, A. V., & Gadekallu, T. R. (2024). GPT (Generative Pre-Trained Transformer)—A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions. IEEE Access, 12, 54608–54649. https://doi.org/10.1109/ACCESS.2024.3389497 ↩︎

  8. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2023). Attention Is All You Need (No. arXiv:1706.03762). arXiv. https://doi.org/10.48550/arXiv.1706.03762 ↩︎

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Natural Language Processing (NLP)Retrieval-Augmented Generation (RAG)