Pingtou's blog

 This is an excellent question that gets to the core value of this new concept. We can explain it by starting with several key pain points and needs. Here are the core reasons why Prompt Enhancement Engineering is necessary: 1. The Limitations of Traditional Prompt Engineering Reliance on Manual Experience, Difficult to Scale: Traditional prompt engineering heavily depends on the personal experience and intuition of engineers or experts. An excellent prompt often requires repeated manual trial and error. This method is inefficient, difficult to scale across large teams or multiple projects, and cannot quickly adapt to new tasks or models. Lack of Systematization and Version Control: Prompts often exist in the form of text files, code comments, or even oral communication, lacking systematic management. This makes it difficult to track the iteration history of a prompt, compare the performance of different versions, let alone conduct A/B testing. Lacks Robustness and Generalization...

Okay, based on the concept of  we can build . We will demonstrate how to use a Large Language Model (LLM) itself to automatically enhance a user's raw prompt. We will use Python and the OpenAI API for this example, as they are simple to use and powerful. System Overview This simple PEE system will follow the core process outlined in our white paper draft: Input: Receive a user's raw prompt. Parsing and Planning: Use an LLM (LLM-as-a-Planner) to analyze the raw prompt and generate an enhancement plan. Enhancement: Based on the enhancement plan, automatically construct a more complete and optimized prompt. Execution and Output: Send the enhanced prompt to an LLM and get the final result. Preparation Before you begin, make sure you have Python installed and an OpenAI API Key. Install the library: Bash pip install openai Set up the API Key: Python import openai import os # It's recommended to use environment variables openai.api_key = os.environ.get( "OPENAI_API...