Similar words.
Different machinery.
Six comparisons for the ideas that are easy to conflate.
Training vs inference ↗
Both run numerical computations through a model. The critical difference is whether the process uses an objective and optimizer to update persistent parameters.
SIDE BY SIDEWeights vs context ↗
A model’s persistent learned numbers and the text supplied to a request are different places for information to influence a prediction.
SIDE BY SIDEBase model vs assistant system ↗
The distinction is a model and product boundary, not a claim that all assistants follow the same recipe.
SIDE BY SIDERetrieval vs fine-tuning ↗
These mechanisms solve different problems and can be combined. One supplies selected information to a request; the other changes learned behavior through updates.
SIDE BY SIDESFT, RLHF & DPO ↗
Compare supervised demonstration learning with two preference-based routes. Post-training is a broad stage, not one compulsory sequence.
SIDE BY SIDEGreedy vs sampling ↗
A probability distribution does not dictate a single output. Decoding decides how the system turns that distribution into a sequence.