GENERATIVE FILLING OF DESIGN CONTENT

1 . A computer system comprising:

one or more processors; and

a memory to store a set of instructions, wherein the one or more processors execute instructions stored in the memory to perform operations comprising:

determining a set of repeating design elements within a design interface;

determining input into a machine learning model that includes (i) example content associated with the set of repeating design elements and (ii) one or more instructions associated with the example content; and

populating at least a portion of the set of repeating design elements with additional content generated by the machine learning model in response to the input.

2 . The computer system of claim 1 , wherein the operations further comprise:

receiving a user input associated with the set of repeating design elements prior to determining that the portion of the design interface includes the set of repeating design elements.

3 . The computer system of claim 2 , wherein the user input comprises a selection that includes the set of repeating design elements.

4 . The computer system of claim 2 , wherein the user input comprises an expansion of the set of repeating design elements.

5 . The computer system of claim 2 , wherein the user input comprises an interaction with a user-interface element associated with generation of the additional content.

6 . The computer system of claim 1 , wherein determining the set of repeating design elements within the design interface comprises:

determining a first match associated with a first plurality of layers in a set of hierarchical structures representing the set of repeating design elements; and

determining one or more additional matches associated with one or more pluralities of layers in the set of repeating design elements, wherein the one or more pluralities of layers are descendants of the first plurality of layers within the set of hierarchical structures.

7 . The computer system of claim 6 , wherein the first match is determined based on a level of similarity among the first plurality of layers.

8 . The computer system of claim 6 , wherein the first match is determined based on one or more attributes associated with the first plurality of layers.

9 . The computer system of claim 1 , wherein determining the one or more instructions comprises:

receiving a custom instruction associated with the additional content from a user.

10 . The computer system of claim 1 , wherein determining the example content comprises extracting the example content from one or more design elements in the set of repeating design elements.

11 . The computer system of claim 1 , wherein the set of repeating design elements comprises at least one of a list or a table.

12 . The computer system of claim 1 , wherein the machine learning model comprises a generative model.

13 . A non-transitory computer-readable medium that stores instructions, executable by one or more processors, to cause the one or more processors to perform operations comprising:

determining a set of repeating design elements within a design interface;

determining input into a machine learning model that includes (i) example content associated with the set of repeating design elements and (ii) one or more instructions associated with the example content; and

populating at least a portion of the set of repeating design elements with additional content generated by the machine learning model in response to the input.

14 . The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise:

receiving a user input associated with the set of repeating design elements prior to determining that the portion of the design interface includes the set of repeating design elements.

15 . The non-transitory computer-readable medium of claim 14 , wherein the user input comprises at least one of a selection of the portion of the design interface, an expansion of the set of repeating design elements, or an interaction with a user-interface element associated with generation of the additional content.

16 . The non-transitory computer-readable medium of claim 13 , wherein determining the set of repeating design elements within the design interface comprises:

determining a first match associated with a first plurality of layers in a set of hierarchical structures representing the set of repeating design elements; and

in response to determining the first match, determining a second match associated with a second plurality of layers in the set of repeating design elements, wherein the second plurality of layers includes children of the first plurality of layers within the set of hierarchical structures.

17 . The non-transitory computer-readable medium of claim 13 , wherein determining the one or more instructions comprises:

determining a system instruction that specifies a role and a task associated with a large language model.

18 . The non-transitory computer-readable medium of claim 13 , wherein the additional content comprises at least one of text content, a layer name, or a visual attribute of a design element.

19 . A computer-implemented method comprising:

determining a set of repeating design elements within a design interface;

determining input into a machine learning model that includes (i) example content associated with the set of repeating design elements and (ii) one or more instructions associated with the example content; and

populating at least a portion of the set of repeating design elements with additional content generated by the machine learning model in response to the input.

20 . The computer-implemented method of claim 19 , wherein the machine learning model comprises a large language model.