提示词工程指南

### Prompt Engineering Guidelines and Tutorials Prompt engineering involves crafting effective prompts to guide large language models (LLMs) towards producing desired outputs. Since basic models understand general English but require more than prompt tuning when specialized knowledge or rules are needed, building custom LLMs might be necessary for advanced applications[^1]. However, mastering the art of prompt design can significantly enhance model performance without always necessitating a fully customized LLM. #### Key Principles in Prompt Design - **Clarity**: Ensure that instructions within the prompt are clear and unambiguous. - **Specificity**: Provide specific details about what kind of response is expected from the model. - **Contextual Information**: Supply relevant background information so the model has enough context to generate accurate responses. - **Format Guidance**: Specify any formatting requirements such as bullet points, tables, etc., directly inside the prompt text. For those looking into learning how to effectively engineer prompts: #### Resources for Learning Prompt Engineering Several online platforms offer comprehensive guides on this topic including detailed explanations along with practical examples which help users gain hands-on experience quickly. Websites like Hugging Face provide not only theoretical insights but also interactive tools where one can experiment with different types of prompts immediately after reading through lessons. Additionally, there exist numerous books dedicated specifically to teaching best practices related to working closely alongside AI systems using natural language processing techniques; these resources often cover both introductory concepts all way up until advanced strategies suitable even for professionals already familiarized somewhat with machine learning algorithms behind modern-day chatbots and virtual assistants today. ```python # Example Python code snippet demonstrating interaction between user input and an LLM via well-crafted prompts def get_model_response(prompt_text): # Assume 'model' represents pre-trained transformer-based architecture here output = model.generate(text_inputs=prompt_text) return output ```