Sources & Attribution
Explore the research foundation behind our prompt engineering taxonomy, based on 17 academic papers and the original compilation by Reddit user u/Background-Zombie689.
Original Reddit Post
Reddit Post
April 2025
I Distilled 17 Research Papers into a Taxonomy of 100+ Prompt Engineering Techniques – Here's the List.
A comprehensive list of over 100 prompt engineering techniques, organized alphabetically and including source citations for each technique.
Research Papers Referenced
General Prompt Engineering Papers
- Schulhoff et al. - "A Survey of Prompt Engineering" Comprehensive survey of prompt engineering techniques across domains.
- Vatsal & Dubey - "Comprehensive Review of Prompt Engineering" Review focusing on state-of-the-art prompt engineering methods.
- Ramnath et al. - "Automatic Prompt Optimization" Explores methods for automating prompt engineering.
- Li et al. - "Optimization Survey" Survey of optimization methods for prompts.
Specialized Technique Papers
- Wei et al. - "Chain-of-Thought Prompting" Original paper introducing CoT prompting.
- Wang et al. - "Self-Consistency" Extension of CoT using multiple paths and voting.
- Zhou et al. - "APE (Automatic Prompt Engineer)" Framework for automatically engineering prompts.
- Ning et al. - "Skeleton-of-Thought" Method for outlining and expanding responses.
- Yao et al. - "Tree-of-Thoughts" Framework for exploring multiple reasoning paths.
- Lewis et al. - "Retrieval-Augmented Generation" Original RAG paper combining retrieval with generation.
- Li et al. - "SCoT (Structured Chain-of-Thought)" Adding structure to CoT for code generation.
- Liu et al. - "LogiCoT" Enhancing CoT with logical reasoning.
- Ridnik et al. - "AlphaCodium" Test-based iterative flow for code generation.
- Lee et al. - "Syntactic Prevalence Analysis" Method for analyzing prompt effects on syntax.
Domain-Specific Papers
- Wang et al. - "Healthcare Survey" Survey of prompt engineering in healthcare.
- Ding et al. - "Cross-File Code Completion" Code-specific prompting techniques.
- Brown et al. - "Few-Shot Learning" Original work on in-context learning.
- Ye et al. - "Prompt-Tuning" Methods for tuning prompts with continuous vectors.
- Honovich et al. - "Instruction Induction" Automating the inference of instructions from examples.
Our Contributions
Extending the Original Research
This website extends the original Reddit post by categorizing the prompt engineering techniques into a structured taxonomy with the following enhancements:
- Organized techniques into logical categories based on function and purpose
- Added detailed descriptions, examples, and use cases for each technique
- Created visualizations to show relationships between techniques
- Developed an interactive prompt builder to create effective prompts by combining multiple techniques
- Built an interactive interface for exploring the taxonomy
- Made all data available in structured JSON format for further research