There are several important requirements for a digital assistant solution. Key functional requirements include:

• Accuracy, explainability, and traceability—Understanding and explaining the reasoning behind the model's predictions or responses is crucial for digital assistants because they disseminate authoritative information. Therefore, accountability, transparency, and ethical considerations are of paramount importance. Any digital assistant architecture must include an authoritative knowledge base in the form of an information retrieval system. The knowledge base allows the establishment of data lineage for any of the model's decisions during inferencing by identifying the document from which the information has been gathered.
• Quality control and error handling—Detecting and handling errors or inaccuracies during inferencing is important to maintain the quality and reliability of the system. Implementing effective error handling, monitoring, and quality control mechanisms to identify and rectify issues is essential. The typical mechanisms for achieving these results is to provide for a human in the loop and Reinforcement Learning with Human Feedback (RLHF).
• Latency and responsiveness—Achieving low-latency and highly responsive inferencing is essential for an interactive application such as a digital assistant. The overall round-trip delay throughout the system (consisting of the steps of request à intent identification à information retrieval à LLM à response) must be considered for the experience to be truly interactive. Balancing the computational demands of the model with the need for fast responses can be challenging, particularly when dealing with high volumes of concurrent user requests.
• Computational resources—High-quality digital assistant implementations are computationally intensive, especially for large and complex applications. Generating predictions or responses in real time requires significant processing power, memory, network, and storage resources.
• Deployment scalability—To handle increasing user demand, scaling up the deployment of a model is needed. Ensuring that the system can handle the required number of concurrent inferencing requests with acceptable latency, and can dynamically allocate resources to meet the workload can be complex, requiring careful architecture design and optimization.

These challenges highlight the need for careful consideration and optimization in various aspects of inferencing, ranging from computational efficiency and scalability to model optimization, interpretability, and quality control. Addressing these challenges contributes to the development of robust and reliable AI systems for powering digital assistants.

Dell Technologies and its partners help solve these challenges by collaborating to deliver a validated and integrated hardware and software solution. This solution is built on Dell high-performance best-in-class infrastructure, and uses the award-winning software stack and the industry-leading accelerator technology and AI enterprise software stack of NVIDIA.