Rubber Duck Debugging with LLMs
Rubber duck debugging is the practice of explaining code to an inanimate object like a rubber duck. In our team we often use this approach given that verbalizing the problem/code often helps programmers gain insight into bugs and logic errors. Large language models (LLMs) can act as virtual rubber ducks for debugging, even if the model is not trained on a brand-new technology. This blogpost explores prompt engineering techniques to optimize LLMs for rubber duck debugging. I strongly recommend following the free short course on Prompt engineering for developers and the new one on Prompt engineering with llama 2.
Different listener personas can be adopted based on roles and expertise levels. For example, you can use the following personas to guide the LLM's response:
"As a debugging assistant, could you walk through steps to fix this recursive Fibonacci function that crashes for large inputs?"
