Existing solutions (e.g., [11, 21, 25, Crafting effective prompts for code generation or editing with Large 30]), while useful, fall short in providing user feedback during the Language Models (LLMs) is not an easy task. Particularly, the absence prompt crafting process. The lack of such feedback can lead to of immediate, stable feedback during prompt crafting hinders uncertainty about the expected output and potential inefficiencies effective interaction, as users are left to mentally imagine possible in the development process, as users are left to mentally imagine outcomes until the code is generated. In response, we introduce possible outcomes until the code is generated [12, 29]. Language-Oriented Code Sketching, an interactive approach that provides However, providing user feedback during prompt crafting presents instant, incremental feedback in the form of code sketches (i.e., significant challenges. Ideally, feedback should not only be instant incomplete code outlines) during prompt crafting. This approach but also evolve incrementally as the user types their prompt, offering converts a prompt into a code sketch by leveraging the inherent clear and understandable intermediate results that progressively linguistic structures within the prompt and applying classic natural guide the LLM towards the final desired code.

We call a memory-mapped file, a file that has its contents directly assigned to a segment of virtual memory, this way we can perform any operations on that segment just like on any other portion of main memory we have access to in the current process. Due to the additional abstraction layer represented by the virtual memory, we can map into memory files that are much larger than the physical capacity of our machine. The segments of memory (called pages) that are required by the running process are fetched from external storage and copied into the main memory automatically by the virtual memory manager.