Pseudo-code prompting uses programming-style syntax with control flow structures to provide instructions to LLMs. By expressing logic as pseudo-code, you leverage the model's extensive training on code to achieve more precise, structured outputs.
This technique provides structural clarity through familiar programming constructs like if/else, loops, and functions.