Inferencing using LLMs for natural language generation in generative AI has numerous practical use cases across various domains. The Generative AI in the Enterprise white paper discusses multiple use cases for generative AI across various industries. Some particular examples of use cases based specifically on inferencing include the following:

• Intelligent documentation creation and processing – Inferencing is used extensively or content generation including natural language and text generation tasks such as document writing, dialogue generation, summarization, or new content creation. It can be used to create drafts, outlines, or final content or documents and reports.  It can be used to generate concise summaries of longer texts, making information more accessible and digestible. It can help in creating technical documentation, providing detailed explanations and instructions. It can assist in language translation, ensuring that documentation is accessible to a wider audience.

With models trained to an organization’s unique data, inferencing can be used effectively for documentation and content creation, so businesses can streamline their processes, improve productivity, and ensure that their content is relevant and of high quality.

• Code generation, assistance, and documentation – Software development can use inferencing applications is multiple ways to assist or automate various tasks. It can perform code generation or code autocompletion, based on high-level descriptions or prompts and based on context, making the writing of code faster and more efficient. It can also assist with refactoring or syntax error detection, generating test cases, and debugging. It can generate documentation or inline comments for code, improving its readability and maintainability. By using inferencing in coding tasks, developers can accelerate their workflow, reduce manual effort, and potentially discover more efficient or optimized solutions.
• Marketing and sales – Inferencing is used in marketing and sales to automate and enhance communication with customers. It allows for personalized responses, product recommendations, and content creation. For instance, in chatbots, it interprets customer queries and generates relevant responses, improving customer support. Additionally, it assists in creating targeted marketing content, such as personalized email messages or social media posts, which helps create market demand and can lead to higher customer engagement and conversion rates.
• Sentiment analysis – Inferencing 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.

Related, Named Entity Recognition (NER) is a natural language processing (NLP) technique used to identify and categorize specific entities within text, to extract and classify them to provide more context and meaning to the text. It works with sentiment analysis, where understanding context is crucial for accurate analysis and responses.

• Customer service – Customer service and support activities use conversational agents, chatbots, and virtual assistants extensively by generating natural language responses based on user queries or instructions. In addition, there are multiple other 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.

While the validation work that we performed in this design is centered primarily on language and text applications such as those applications described above, there are also other non-LLM 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 such as 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.