Few-shot prompting is a pretty common technique used for LLMs. By providing a few examples of your data in the prompt, the model learns "on the fly" and produces better results — but what happens if the examples you provide are error-prone?
I spent some time playing around with Open AI's davinci LLM and I discovered that real-world data is messy and full of issues, which led to poor quality few-shot prompts and unreliable LLM predictions.
I wrote up a quick article that shows how I used data-centric AI to automatically clean the noisy examples pool in order to create higher quality few-shot prompts. The resulting predictions had 37% fewer errors than the same LLM using few-shot prompts from the noisy examples pool.
Let me know what you think!