Definition
The process of using a trained AI model to make predictions or generate outputs on new data — the 'production' phase of AI.
In Depth
Inference is when a model processes new input and produces output (as opposed to training, where it learns from data). Inference speed and cost are critical production concerns — they determine API response times and per-request costs. Optimization techniques include batching, caching, quantization, and model distillation.
Example Usage
Each API call to Claude for content generation is an inference request — optimizing prompt length reduces inference cost and latency.
Business Context
Inference costs are the primary ongoing expense for AI features. At scale, the difference between a 500-token and 2000-token prompt translates to significant cost savings.
Related Terms
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