AI researcher and author specializing in large language models (LLMs) and their practical applications.

Systematic exploration of prompt engineering techniques to enhance LLM performance

Michael Sherman

Yuan Cao

Erick Armbrust

Anant Nawalgaria

Antonio Gulli

Simone Cammel

Antonio Gulli

Anant Nawalgaria

Grace Mollison

Joey Haymaker

Michael Lanning

LLM outputs heavily depend on prompt quality

Complexity in balancing factors like model type, training data, and language style

LLM limitations in logical reasoning and factual accuracy

Model Selection

Prompt Design

Parameter Optimization

Iterative Refinement

Documentation

Explanation of sampling methods

Importance of configuration settings

Output length

Temperature

TopK and TopP

Use low temperature (e.g., 0) for code generation or factual tasks

Combine Top-K and Top-P to avoid extreme randomness

Code Generation

Code Translation

Debugging

Always test generated code for accuracy and edge cases

Use concise, unambiguous instructions (e.g., "Output JSON" vs. "Avoid paragraphs")

Enforce formats like JSON/XML for consistency and downstream integration

Frame prompts with directives (e.g., "Use formal tone") instead of constraints

Use placeholders (e.g., [TOPIC]) for reusable prompts

Test different input formats (questions vs. commands) and document results

Decompose complex problems into intermediate steps

Generate multiple reasoning paths and select the most coherent answer via majority voting

Use phrases like "Let’s solve this step-by-step to ensure accuracy"

Prompt engineering is iterative and essential for leveraging LLM potential

Techniques like CoT, ReAct, and APE enhance reasoning, tool integration, and scalability

Future trends include multimodal prompts (text + image/audio) and AI-generated prompts

Optimized prompts can increase accuracy by 58% and code generation efficiency by 120%

Includes studies on APE, ToT, and ReAct from academic papers and Google’s internal research

Explore tools like PromptPerfect for automated optimization and Langchain for task orchestration