Learn the controls and knobs

Primary knobs for training vs inference: learning rate, batch size, epochs, regularization, checkpoint pick, inference precision, decode settings.

Write a sheet for the ticket classifier service with columns: control, current value, predicted benefit, predicted cost, rollback trigger. Fill it using this topic’s real tension: Training is expensive but amortized across many uses and can change persistent behavior. Inference is repeated per request and dominates operating cost at scale. Fine-tuning can improve stable task behavior but is slower to refresh than prompts or RAG for changing facts.

Change one training vs inference family at a time. If you move two knobs and the ticket classifier service improves, you learned a cocktail, not a cause—and you cannot roll back surgically.

End users of the ticket classifier service should see only safe dials related to training vs inference. Infrastructure limits, private prompts, and policy thresholds stay server-owned. A user-facing control that bypasses those limits is a vulnerability dressed as UX for training vs inference.

Publish the training vs inference control sheet next to the ticket classifier service runbook. On-call should see which knob moved in the last deploy without reading chat archaeology. Unknown training vs inference knobs are unowned knobs.

Also pin one numeric memory from this training vs inference chapter: one epoch over 80,000 examples with batch size 100 requires 800 optimizer steps; five epochs require 4,000 steps That number is not decoration; it is a template for how claims about training vs inference on the ticket classifier service should look in design docs. Scoped specifically to training vs inference / ticket classifier service / controls-and-knobs.

People confuse training vs inference with neighboring buzzwords when debugging the ticket classifier service. Before changing prompts, ask whether the broken stage was evidence gathering, the training vs inference judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried training vs inference and it failed”) that blocks the next team on the ticket classifier service. Scoped specifically to training vs inference / ticket classifier service / controls-and-knobs.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to training vs inference rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to training vs inference rather than generic AI advice.