The process of using a trained AI model to make predictions or generate outputs on new data — the 'production' phase of AI.
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.
Each API call to Claude for content generation is an inference request — optimizing prompt length reduces inference cost and latency.
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.
The field focused on ensuring AI systems behave as intended, don't cause harm, and remain aligned wi...
A neural network component that allows models to focus on the most relevant parts of the input when ...
AI systems that can interpret and understand visual information from images and video.
Numerical representations of text that capture semantic meaning for AI processing.
An LLM capability that allows the model to invoke predefined functions or APIs as part of its respon...
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