Inference
Inference is the process of using a trained AI model to make predictions or generate outputs on new, previously unseen data in real time.
In Depth
While training creates the AI model, inference is where it actually does useful work. Every time an AI agent reads a customer message and generates a response, that is inference. Inference performance is measured by latency (how fast the model responds), throughput (how many requests it can handle simultaneously), and accuracy (quality of the output).
In customer support, inference speed directly impacts customer experience — responses need to feel near-instantaneous in live chat, even if the underlying model is processing complex reasoning chains. Optimizing inference involves techniques like model quantization (reducing precision for faster computation), caching (storing common responses), batching (processing multiple requests together), and edge deployment (running models closer to users). Cost management is also critical, as inference costs scale with usage volume.
Related Terms
Large Language Model
A large language model (LLM) is a deep learning model trained on vast amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
Model Training
Model training is the process of teaching an AI system to recognize patterns, make predictions, or generate outputs by exposing it to labeled or unlabeled data and adjusting its parameters.
AI Agent
An AI agent is an autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals without continuous human intervention.
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