Large language models (LLMs) have revolutionized code generation, but their ability to adapt existing code snippets remains a challenge. Imagine trying to fit a pre-fabricated piece of furniture into a room with unique dimensions – it requires more than just assembly; it demands careful modification and adjustment. Similarly, adapting a code snippet requires understanding the existing codebase (the room) and modifying the snippet (the furniture) to fit seamlessly. This research delves into the intricacies of code snippet adaptation using LLMs, revealing why they struggle and proposing innovative solutions through prompt engineering. The study finds that LLMs often exhibit 'laziness,' making minimal changes to the original snippet and overlooking existing defects. They can also misinterpret the surrounding code, leading to errors. Think of it like an LLM trying to replace a lightbulb without understanding the wiring – it might get the bulb in, but the circuit won't work. The researchers explored different prompting techniques, including enriching the context with more information about the codebase and breaking down the adaptation task into smaller, more manageable steps. They also introduced a novel 'flipped interaction' approach, where the LLM actively asks questions to clarify requirements, much like a human developer would. This interactive approach, particularly when guided by a human expert, significantly improved the LLM's adaptation abilities, demonstrating its potential for automating this crucial aspect of software development. While human-guided interaction yielded the best results, the study also explored multi-agent interactions, where one LLM acts as a 'counselor' to another, providing feedback and guidance. This automated approach offered a promising trade-off between performance and human effort, paving the way for more efficient and autonomous code adaptation using LLMs in the future. This research sheds light on the nuances of using LLMs for complex coding tasks and provides a roadmap for future advancements in AI-powered software development.