Inference

Inference is the process of running a trained AI model to generate outputs (predictions, text, images) based on new input data. If training is "learning," inference is "applying what was learned." Every time you send a message to ChatGPT, Claude, or an AI agent, you're triggering an inference.

Key aspects of inference include: input processing (tokenizing and encoding the user's message plus context), model computation (the neural network processes the input through its layers), output generation (producing tokens one at a time for text generation), and post-processing (formatting, safety filtering, and delivering the response).

Inference performance is measured by: latency (time to first token and total response time), throughput (how many requests per second the system handles), cost (price per token or per request), and quality (accuracy and helpfulness of outputs).

Inference optimization techniques include: model quantization (reducing model precision for faster processing), batching (processing multiple requests simultaneously), caching (storing common responses), speculative decoding (using smaller models to draft, larger models to verify), and hardware optimization (using specialized chips like GPUs, TPUs, and inference accelerators).

For AI agent builders, inference costs are a key consideration. Each conversation turn requires an inference call, and costs scale with usage. Strategies to manage inference costs include: choosing the right model size for the task, optimizing system prompts to reduce token usage, caching common queries, and using tiered models (simpler model for easy questions, powerful model for complex ones).