Generative AI (GenAI): Definition, Models, and Practical Application
Generative AI (GenAI) is a subfield of Artificial Intelligence focused on creating new content – from text and photos to music, audio, films, and 3D representations. According to HPE, these models learn complex patterns and structures from training data and then independently produce new material with comparable characteristics. GenAI is therefore not a single application type, but a class of methods and models. For companies looking to automate processes, understanding this class is a fundamental prerequisite.
What is Generative AI?
GenAI models synthesize new outputs from learned relationships. HPE describes the use of advanced algorithms and neural networks as the technical basis. This foundation enables creative content production in areas such as robotics, design, creative arts, and entertainment. The term encompasses a wide range of methods – not just language models.
How Does Generative AI Work?
At the model and method level, HPE distinguishes several categories of generative techniques:
- Generative Adversarial Networks (GANs): Two neural networks – a "Generator" and a "Discriminator" – work against each other to produce realistic outputs.
- Variational Autoencoders (VAEs): They learn a compressed representation of the data and generate diverse outputs through probabilistic elements.
- Autoregressive Models: They model conditional probabilities and predict – as with GPT – the next word.
- Transformer-based Models: Relevant for translation, summarization, and text generation.
- Recurrent Neural Networks (RNNs): Working with feedback connections for sequential data.
- Reinforcement Learning: Also used for generative tasks.
To distinguish, HPE refers to rule-based systems, which are based on predefined rules and logical reasoning – and thus not on learning-based generation from training patterns.
Generative AI in Practice
Many organizations initially deploy GenAI as a chatbot interface – for example, in the form of ChatGPT as a conversational and answering tool. However, according to a Deloitte TechPulse article, this only represents a fraction of its true potential. The problem: Most GenAI interactions are currently prompt-based. Writing and refining prompts is time-consuming and skill-intensive, especially for repetitive tasks.
The alternative is a systematic approach: GenAI applications are built as reusable "blueprint" implementations – creatable via no-code/low-code tools. Predefined prompts and task logics are prepared in such a way that users typically only need to input relevant data. The system then operates based on this structure, without the need for manual re-prompting.
Deloitte also describes the use of Feedback Loops: Business units provide feedback after go-live, and prompt engineering is iteratively adjusted in the background. Additionally, integrations with external sources and internal systems can be implemented to better anchor outputs.
Practical Examples and Use Cases
Deloitte cites a life sciences company as a concrete example. There, GenAI is used to create documents for IT, Legal, and HR – each tailored to the specific workflows and data of the department:
- IT: Compliance-related code reports
- Legal: Contract drafts with policy checks
- HR: Validated Offer Letters
These use cases demonstrate that GenAI is not used as a generic tool, but as a domain-specific pre-configured system.
Conclusion
Generative AI refers to AI models that create new content by learning complex patterns from data. The public focus is often on chatbots – however, the greater benefit comes from reusable, domain-specific pre-configured applications with iterative prompt engineering and system integration. Those who integrate GenAI systematically into business processes can significantly reduce the effort for manual prompt work.