Before we dive into the benefits of generative AI for the enterprise, it's important to understand the key terms in this field:

• Generative Adversarial Networks (GANs): A neural network framework used to generate new data similar to a given set of training data.
• Natural Language Processing (NLP): A branch of AI that enables a computer to understand and analyze human language.
• Large Language Model (LLM): A type of GAN that can perform NLP tasks, including generating text, and answering questions in a conversational manner.
• Transformer Model: A neural network architecture that enables an LLM to process and learn from sequences of words, recognizing patterns and relationships within the text.
• Pre-trained Model: A machine learning model created by an external developer, used to solve deep learning problems, and trained on a large dataset.
• Domain models: Domain models combine the fluency and comprehensibility of emerging transformers with the domain-specific data and knowledge of an enterprise.
• Fine-tuning: The process of taking a pre-trained model and adapting it to a specific domain by training it on a smaller dataset.
• Hallucination: A situation where the LLM generates output that is not supported by the input data or is inaccurate.
• Retrieval-based Large Language Model: An LLM that generates responses using integrated source information, including a company’s application data or a corpus of documents. These models address the shortcomings of traditional LLMs that generate incorrect answers if asked a question they haven’t been trained on.
• Enterprise AI: Providing predictive insights to expert users for improving performance of enterprise processes and systems. (See What Is Enterprise AI?)
• Generative AI for Enterprise Search – Leveraging generative AI’s intuitive search bar to access the predictive insights and underlying data of enterprise AI applications, improving decision-making for all enterprise users.