Ụdị AI kọwara

A complete AI product also includes data processing, interfaces, retrieval, tools, and operating rules. A model name alone does not describe that entire product.

Isi ihe na-ewe

• Separate the model from the product around it.
• Distinguish learned parameters from training settings.
• Select using the application’s constraints and measured errors.

Different models represent different kinds of relationships. A linear model combines weighted features. A decision tree follows learned splits. A neural network combines parameterized transformations across layers. Choosing among them depends on the problem, available examples, computational limits, and the kind of explanation users need. Training selects parameter values. Hyperparameters, such as a tree-depth limit or a learning rate, govern the learning procedure or model structure and are usually selected through validation. Confusing these two makes experiments difficult to reproduce. A foundation model can be adapted to multiple tasks, but that flexibility does not remove evaluation requirements. Prompting, fine-tuning, and retrieval change different parts of a system. A retrieved document may update available evidence without changing weights; fine-tuning changes the weights without guaranteeing current information. Compare candidates on a fixed set of representative inputs. Record errors, latency, memory, and failure handling, not just a leaderboard score. Prefer the simplest option that meets the task requirements. When changing a model version, repeat the comparison because interfaces can remain stable while behavior changes.

1. Suppose a team needs to label documents within 100 ms. In an illustrative test, model A reaches 92% accuracy at 30 ms and model B reaches 94% at 400 ms.
2. Inspect the two models’ errors and whether the additional correct labels matter enough to change the latency requirement.
3. If 100 ms is a firm constraint and model A meets the error tolerance, it is the viable candidate for this particular deployment.

The invented comparison shows a task-specific choice, not a ranking of model families.