The word “inference,” in English, means a conclusion drawn through reasoning and evidence. Similarly, AI inference relates to an AI model’s ability to infer, or extrapolate, conclusions in new situations, using information gained from training, response, and the fine tuning process. In short, AI inference is the process of using AI models to generate predictions or outputs from new data.
For example, an AI system trained on thousands of past customer support tickets learns how issues were categorized and resolved. During inference, when a new ticket arrives, the system can classify the problem, suggest the most likely solution, and route it to the appropriate team. It is now applying its knowledge base to analyze a case it has never seen before.
This article explains AI inference, its types, and the challenges organizations must overcome to successfully run AI at scale.
AI inference is the stage where a trained AI system is used to predict or generate new data based on real world use cases. The most common example today is inputting a prompt to ChatGPT or any LLM and having it generate the output response to an inquiry.
These AI systems can be thought of as digital workers of the modern enterprise. Like any new employee, they require training so they understand the information and context they have to work with and the boundaries of their operations. The data may be relevant examples or past data. Once ingested, the system begins inference, meaning it applies what it has learned to new situations it has never encountered before.
