Resources

Prompting

Prompt Best Practices

The fundamentals that work across all LLMs:

  • Role/Persona: Who the AI should act as

  • Context: What background info it needs

  • Task: What you want it to do explicitly

  • Format: How you want the output structured

  • Constraints: What to avoid or limits to follow


Source: Reddit: Best Practices for AI Prompting 2025?

CO-STAR Prompt Framework

A great prompt usually includes these specific elements to provide context and direction:

  • Context: Provide background information on the task.

  • Objective: Define exactly what you want me to do.

  • Style: Specify a writing style (e.g., professional, humorous, poetic).

  • Tone: Set the emotional vibe (e.g., empathetic, direct, enthusiastic).

  • Audience: Who is this for? (e.g., a CEO, a 5-year-old, a software engineer).

  • Response: Define the format (e.g., a table, a list, a JSON object).

Additional Components

  • Few-Shot Prompting/Using Examples: Provide examples of desired output and formatting (e.g., programming language, file type [CSV, Markdown, PNG])

  • Use Delimiters: Use triple quotes (“““), XML tags (), or dashes (—) to wrap your content (e.g., Summarize the text delimited by triple quotes:”““[Insert article]”““)


Source: Google Gemini (Prompt: do you have any prompt engineering best practices)

Additional Prompt Engineering and Other Resources

Anthropic

OpenAI

Google

10 Best Practices for Prompt Engineering With Any Model

GitHub: Awesome AI System Prompts

Coding and Data Analysis with LLMs

Integrated Development Environments (IDEs)

VS Code

Positron

Coding LLMs

Claude Code

Codex

Google AI Studio

Cursor

Melissa Van Bussel posit::conf 2024 Keynote

Additional Resources

Chicago Booth Algorithmic Bias Initiative

EHI Living Manual (EPIC)

PubMed API

Visualize Effects of Temperature, Top-P, and Top-K Parameters