Essential Prompting Frameworks for Crafting Powerful AI Interactions with ChatGPT and Bard

Introduction to Large Language Models

The advent of Large Language Models (LLMs) like ChatGPT and Bard has revolutionized AI’s role in creativity and problem-solving. To fully leverage these tools, one must become adept in prompt engineering – the skill of crafting instructions and information that steer LLMs toward desired outcomes.

Frameworks for Effective Prompt Engineering

1. APE: Action, Purpose, Expectation

• Action: Define the specific task (e.g., write a poem, translate text).
• Purpose: Clarify the goal behind the task (e.g., express emotions, communicate information).
• Expectation: Set clear outcomes (e.g., a creative poem, accurate translation).

Example Prompt:

• Action: Compose a nature-themed poem.
• Purpose: Evoke awe and wonder.
• Expectation: Utilize vivid imagery for a creative piece.

2. CARE: Context, Action, Result, Example

• Context: Offer background information for better understanding.
• Action: Specify the task (e.g., create a poem, generate code).
• Result: Expectation of the outcome (e.g., evocative poem, efficient code).
• Example: Provide concrete examples for guidance.

Example Prompt:

• Context: Earth Day school assembly.
• Action: Write a nature-themed poem.
• Result: A poem for young audiences with descriptive language.
• Example: Include elements like the grandeur of an oak tree.

3. CREATE: Character, Request, Examples, Adjustments, Type of Output, Extras

• Character: Assign a persona for the LLM (e.g., a wizard, comedian).
• Request: Define the specific task (e.g., write a joke).
• Examples: Offer examples to set style and tone.
• Adjustments: Fine-tune instructions and style.
• Type of Output: Determine the format (e.g., script, song).
• Extras: Add additional instructions or keywords.

Example Prompt:

• Character: A humorous stand-up comedian.
• Request: Create a joke about online dating.
• Examples: Incorporate programmer humor.
• Adjustments: Blend in self-deprecating humor.
• Type of Output: A comedy monologue.

Conclusion

These frameworks are starting points for refining your prompt engineering skills. The key is to experiment and adapt to the task at hand, unlocking the full potential of LLMs for both creative and analytical applications.