The article discusses the rise of prompt engineering as a discipline for tuning prompts to interact with large language models (LLMs) effectively. It addresses the challenges of curating and maintaining a high-quality prompt store, highlighting the difficulties due to overlapping prompts. It uses content writing as an example to illustrate the need for a systematic approach to retrieving optimal prompts.
