AI inferencing is the process of using a pre-trained model, such as GPT-4 or Claude 3, to analyze new data and generate inferences or probable outcomes. This technique is commonly applied in areas like chatbots, coding assistance, and image recognition. However, traditional AI models may struggle with specific queries that require proprietary data not included in their training.

RAG enhances AI inferencing by integrating external data sources that the original model was not trained on. This connection to domain-specific data allows the model to produce more accurate and relevant outputs. For instance, an insurance model trained on general population data can provide better insights when supplemented with specific customer data.

Cisco AI PODs for Inferencing are CVD-based solutions designed for Edge Inference, RAG, and Large-Scale Inferencing. They facilitate accelerated deployment with centralized management and automation. These solutions have been performance tested to demonstrate linear scalability, ensuring consistent performance across varying dataset sizes, making them suitable for both data center and edge AI deployments.