文件列表:

pos_data/ (0, 2023-11-11)
pos_data/code.py (4385, 2023-11-11)
pos_data/pos_data.ipynb (63850, 2023-11-11)
pos_data/prompt.txt (6622, 2023-11-11)
prompt_engineering/ (0, 2023-11-11)
pyspark/ (0, 2023-11-11)
pyspark/aws_glue_pyspark.py (2009, 2023-11-11)
pyspark/aws_lambda.py (557, 2023-11-11)
pyspark/gcp_bigquery.py (698, 2023-11-11)
pyspark/goolge_drive.py (284, 2023-11-11)
pyspark/prompt.txt (3080, 2023-11-11)
python/ (0, 2023-11-11)
recceipt_data/ (0, 2023-11-11)
recceipt_data/code.py (2623, 2023-11-11)
recceipt_data/prompt.txt (3910, 2023-11-11)
recceipt_data/receipt_data.ipynb (7037, 2023-11-11)
sql/ (0, 2023-11-11)
tips/ (0, 2023-11-11)
titanic/ (0, 2023-11-11)

# chatgpt playing with ChatGPT ## Prompt engineering Prompt engineering designs and creates prompts or queries in natural language processing (NLP) and conversational AI systems. It involves crafting well-structured and meaningful questions or instructions to elicit desired responses from AI models. Prompt engineering is commonly used in various NLP applications, including chatbots, search engines, and language models like GPT-3.
### Here are critical aspects of prompt engineering: Clarity: > Prompts should be unambiguous, making it easy for users to understand and interact with the AI system. Ambiguity or clarity in prompts can lead to accurate or unintended results.
Specificity: > Prompts must be specific and provide a clear context for the AI model. The more specific the prompt, the more likely the AI system will produce relevant and accurate responses.
Intent: > Prompt engineering focuses on capturing the user's intent accurately. You can guide the AI model to provide the desired information or actions by crafting prompts that reflect the user's intent.
Language Variation: > Language is complex and can have many variations. Effective, prompt engineering should consider these variations, including how users phrase their requests.
Ethical Considerations: > When designing prompts, it's important to consider ethical considerations, such as avoiding biases, offensive content, or requests that violate privacy or ethical guidelines.
Testing and Iteration: > Prompt engineering often involves a process of testing and iterating prompts to find the most effective ones. This may involve gathering user feedback and analyzing the performance of the AI system.
Preprocessing: > Sometimes, prompt engineering may involve preprocessing user input to ensure it is suitable for the AI model. This may include tasks like spell-checking or grammar correction.
Effective, prompt engineering is crucial for creating user-friendly and reliable AI applications. > It plays a significant role in ensuring that AI models understand and respond to user queries accurately and meaningfully.

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