Precise Optimization Solutions for Academic Scenarios
Available through PromptReady's academic-specific optimization module:
- Element structuring::
1. Mandatory inclusion of academic elements (methodology/data sources/references)
2. Automatic addition of restrictions (e.g., "based on SCI papers in the last 5 years")
3. Standardizing output formats (APA/MLA, etc.) - Subject customization optimization::
1. STEM fields: emphasizing data accuracy and experimental design
2. Humanities: highlighting theoretical frameworks and critical analysis
3. Cross-cutting disciplines: automatic inclusion of cross-cutting comparison prompts - verification mechanism::
1. Flagging of suspicious statements using the plug-in "Academic Verification" function
2. Generate validation questions ("Please provide specific research to support this conclusion")
3. Set up fact-checking instructions ("cite only peer-reviewed literature")
Practical examples: input "summarize machine learning papers" can be optimized as "use 500 words to summarize supervised learning papers published by Nature in 2020-2023, comparing the accuracy difference between SVM and neural network, with 3 key documents DOI ", which significantly improves the reliability of the information.
This answer comes from the articlePromptReady: a Chrome plugin to optimize AI prompt wordsThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of using ChatGPT for information in academic research?
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