Resources

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Schulhoff et al.

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

White et al.

ChatGPT Prompt Patterns for Improving Code Quality

White et al.

A Systematic Survey of Prompt Engineering in LLMs

Sahoo et al.

Pre-train, Prompt, and Predict

Liu et al.

Buffer of Thoughts: Thought-Augmented Reasoning

Yang et al.

Lost in the Middle: How Language Models Use Long Contexts

Liu et al.

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick et al.

Tree of Thoughts

Yao et al.

Chain-of-Thought Prompting

Wei et al.

Reflexion

Shinn et al.

Large Language Models Are Human-Level Prompt Engineers

Zhou et al.

Self-Consistency Improves Chain-of-Thought Reasoning

Wang et al.

Language Models Are Zero-Shot Reasoners

Kojima et al.

Unleashing the Potential of Prompt Engineering for LLMs

Chen et al.

Prompting in the Wild

Tafreshipour et al.

Text Data Augmentation for Large Language Models

Chai et al.

Prompt Engineering (Whitepaper)

Lee Boonstra

Reprompting

Xu et al.