Achieving 5x Agentic Coding Performance with Few-Shot Prompting

Few-shot prompting is a technique to significantly improve Large Language Model (LLM) performance by providing examples of previous work. This method reduces ambiguity by showing the model the desired output style or structure, rather than just describing it in a prompt. To use this effectively, one should organize past work into accessible folders and reference these examples when initiating new, similar tasks. Practical applications include replicating code with modifications, creating consistently styled marketing materials, and generating new slash commands based on an established format.