Inference is the process where an AI model applies what it has learned during training to make predictions or decisions about new, unseen data – similar to how an experienced wine taster can identify the characteristics of a new wine based on their previous tasting experience. It’s the “production” phase of an AI system, where the model is actually doing the job it was trained to do.
Just as a human expert draws upon their accumulated knowledge to make judgments, during inference, an AI model uses the patterns and relationships it learned during training to process new inputs and generate appropriate outputs. For instance, when you prompt an AI like Claude with a question, and it responds, that’s inference in action – the AI is applying its trained understanding to analyze your specific prompt.
Think of inference like a master chef using their culinary training and experience to create a dish from new ingredients. The chef isn’t learning new cooking techniques at this point; they’re applying their existing expertise to produce results. Similarly, during inference, an AI model isn’t learning anything new – it’s simply using its “expertise” to process new inputs.
From a business perspective, inference is typically where most of the ongoing costs and operational considerations come into play. While training might be a one-time intensive process, inference happens every time a user interacts with the AI system. This is why businesses often focus on optimizing inference speed and cost, as it directly impacts user experience and operating expenses.