Application Development with Foundation Models

When people ask what makes one AI product better than a competitor's, they usually think first about the model. That instinct is reasonable in some settings, but it stops working when the model is no longer something a single team owns. This article follows that change: why the model's quality stops being a dependable advantage, and what the application development layer offers in its place.

Compare two settings. In the first, a team builds its own machine learning model from the ground up. The model is proprietary, meaning only that team has it. In that setting the quality of the model can itself be a differentiator. If one team's model is better than another's, customers have a reason to prefer it, and the team can point to the model as the reason.

Now consider the second setting, the one foundation models create. Many teams use the same model. Because the model is shared, its quality no longer belongs to any single team, so it cannot be what distinguishes one product from another. The model stays the same across teams; what changes from team to team is the work built on top of it.

The source names the place where differentiation is gained: the application development layer. It is made up of three responsibilities, namely evaluation, prompt engineering, and AI interface. Think of the layer as a container with three parts. Its job is to hold the work a team does on top of a foundation model, and that work is what the team controls even when the model is shared.

The layer sits above the model. The model is the shared foundation; the layer is the set of responsibilities each team carries out on top of it. The diagram shows the grouping, which is the main idea to take away from this section.

The layer model becomes useful when you ask, for a specific decision, which responsibility it falls under. The examples in this section are original illustrations, not drawn from the source, so they show how the grouping can be applied rather than being quoted facts.

Notice what the matching does not require. You do not need to know how the shared model was trained to decide where Team A's change belongs. You only need to ask whether the decision is something a team does on top of the model, and if so, which of the three responsibilities it serves.

Trace the argument step by step using the shared-model setting. Start with the fact that the model is shared. Next, ask whether that shared fact can separate one product from another. The source says it cannot by itself. Finally, move to the layer, where the team's own work lives. This sequence is the core reasoning of the whole article, and it applies to any product decision you meet.