Inferencing using LLMs for natural language generation in generative AI has numerous practical use cases across various domains. While the Generative AI in the Enterprise white paper discussed some use cases for generative AI for various industries, some notable examples of use cases based specifically on inferencing include:

• Natural language generation—Generative AI models can be used for text generation tasks such as document writing, dialogue generation, summarization, or content creation for marketing and advertising purposes.
• Chatbots and virtual assistants—Generative AI powers conversational agents, chatbots, and virtual assistants by generating natural language responses based on user queries or instructions.
• Personalized recommendations—Generative AI can generate personalized recommendations for products, movies, music, or content based on user preferences, behavior, and historical data.
• Data augmentation—Generative AI can generate synthetic data samples to augment existing datasets, increasing the diversity and size of the training data for machine learning models.
• Customer service and troubleshooting—In addition to chatbots and virtual assistants, there are a number of applications of inferencing in customer service and troubleshooting environments, including applications such as:

• Self-service knowledge bases—Generative AI can automatically generate and update knowledge base articles, FAQs, and troubleshooting guides. When customers encounter issues, they can search the knowledge base to find relevant self-help resources that provide step-by-step instructions or solutions.
• Contextual responses, problem solving, and proactive troubleshooting—Generative AI can analyze customer queries or problem descriptions and generate contextually relevant responses or troubleshooting suggestions. By understanding the context, the AI system can offer tailored recommendations, guiding customers through the troubleshooting process.
• Interactive diagnostics—Generative AI can simulate interactive diagnostic conversations to identify potential issues and guide customers towards resolution. Through a series of questions and responses, the system can narrow down the problem, offer suggestions, or provide next steps for troubleshooting.
• Intelligent routing and escalation—Generative AI models can intelligently route customer inquiries or troubleshoot specific issues based on their complexity or severity. They can determine when a query must be escalated to human support agents, ensuring efficient use of resources and timely resolution.
• Sentiment analysis and customer sentiment monitoring—Generative AI can analyze customer sentiment and emotional cues from their messages or interactions. This analysis allows organizations to monitor customer satisfaction levels, identify potential issues, and take proactive measures to address concerns.

While the validation work that we performed on this design is centered primarily on text and language applications such as those applications described above, there are other use cases of generative AI inferencing that include:

• Image synthesis—Generative AI models can generate initial realistic images or modify existing images by applying various transformations, such as style transfer, image inpainting, or super-resolution.
• Music composition—Generative AI can create music compositions, harmonies, melodies, or even entire music tracks in various genres or styles based on the learned patterns from training data.
• Video generation—Generative AI models can synthesize new video content or modify existing videos, enabling applications like video completion, deepfake creation, or video enhancement.
• Virtual worlds and environments—Generative AI can generate virtual worlds, landscapes, or architectural designs for use in video games, virtual reality (VR), or simulations.
• Virtual characters and avatars—Generative AI can create virtual characters, avatars, or digital personas that exhibit specific traits, behaviors, or personalities.

These examples show how inferencing in generative AI is applied across various domains. The versatility of generative AI allows for creative and innovative applications in content generation, creative arts, virtual environments, personalization, customer service, and more. The use cases continue to expand as generative AI technologies advance.