The Prompt Report: A Systematic Survey of Prompting Techniques
"The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions.
Key Points
- A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document.
- The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks.
- In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques.
- Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts.
- Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated.
- Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques.
- The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs.
https://learnprompting.org/blog/the_prompt_report
https://huggingface.co/papers/2406.06608
https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques
#PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools
"The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions.
Key Points
- A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document.
- The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks.
- In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques.
- Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts.
- Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated.
- Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques.
- The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs.
https://learnprompting.org/blog/the_prompt_report
https://huggingface.co/papers/2406.06608
https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques
#PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools
The Prompt Report: A Systematic Survey of Prompting Techniques
"The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions.
Key Points
- A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document.
- The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks.
- In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques.
- Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts.
- Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated.
- Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques.
- The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs.
https://learnprompting.org/blog/the_prompt_report
https://huggingface.co/papers/2406.06608
https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques
#PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools
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