Key Prompting Strategies

• Basic Prompting: Simple queries without specific enhancements.
• Chain-of-Thought (CoT): Breaks problems into intermediate reasoning steps, improving tasks like mathematical problem solving and commonsense reasoning.
• Self-Consistency: Generates multiple reasoning paths to ensure reliable answers, refining CoT outputs.
• Automatic Chain-of-Thought (Auto-CoT): Automates reasoning chains, eliminating the need for curated examples.
• Program-of-Thoughts (PoT): Combines LLMs with Python for accurate numerical reasoning by delegating computation to an interpreter.

Applications of Prompt Engineering

These methods are used across diverse NLP tasks such as:

• Mathematical Problem Solving: Using CoT, Self-Consistency, and PoT for accurate computations.
• Logical Reasoning: Techniques like Analogical Reasoning and Chain-of-Verification enhance logical deductions.
• Commonsense Reasoning: Strategies like Auto-CoT and Maieutic Prompting improve practical knowledge extraction.
• Multi-Hop Reasoning: Tools like Decomposed Prompting connect dispersed evidence for complex queries.

Benefits of Prompt Engineering

By optimizing prompts, users can:

• Maximize the performance of LLMs on specific tasks.
• Avoid costly retraining by leveraging the pre-trained capabilities of LLMs.
• Enable broader accessibility for individuals without deep expertise in machine learning.