Summary of Prompt Patterns for Conversational LLMs

 Here is a summary of the prompt patterns described in the document, along with original examples:

1. Meta Language Creation Pattern ​

  • Purpose: Define a custom language or shorthand notation for the LLM to understand. ​
  • Example: "From now on, whenever I type two identifiers separated by a '! ​', I am describing a graph. ​ For example, 'a ! ​ b' means a graph with nodes 'a' and 'b' and an edge between them." ​

2. Output Automater Pattern ​

  • Purpose: Generate scripts or automation artifacts to perform recommended steps. ​
  • Example: "Whenever you generate code that spans more than one file, create a Python script to automatically create the specified files or make changes to existing files." ​

3. Flipped Interaction Pattern ​

  • Purpose: Have the LLM ask questions to gather information and achieve a goal. ​
  • Example: "Ask me questions to deploy a Python application to AWS. ​ When you have enough information, create a Python script to automate the deployment." ​

4. Persona Pattern

  • Purpose: Assign a specific role or perspective to the LLM. ​
  • Example: "Act as a security reviewer. ​ Pay close attention to the security details of any code we look at and provide outputs that a security reviewer would." ​

5. Question Refinement Pattern

  • Purpose: Suggest better versions of user questions for improved accuracy. ​
  • Example: "Whenever I ask a question about software security, suggest a better version that incorporates specific risks in the language or framework I am using." ​

6. Alternative Approaches Pattern ​

  • Purpose: Offer alternative ways to accomplish a task and compare pros/cons. ​
  • Example: "If there are alternative ways to deploy an application to AWS, list the best options and compare them based on cost, availability, and maintenance effort." ​

7. Cognitive Verifier Pattern ​

  • Purpose: Subdivide a question into smaller questions for better reasoning. ​
  • Example: "When I ask a question, generate three additional questions to help you give a more accurate answer. ​ Combine the answers to produce the final response." ​

8. Fact Check List Pattern ​

  • Purpose: Generate a list of facts in the output for verification. ​
  • Example: "When you generate an answer, create a list of facts related to cybersecurity that should be fact-checked and include it at the end of your output."

9. Template Pattern

  • Purpose: Ensure output follows a specific format or structure. ​
  • Example: "Use this template for your output: https://myapi.com/NAME/profile/JOB. ​ Everything in all caps is a placeholder." ​

10. Infinite Generation Pattern ​

  • Purpose: Continuously generate outputs without re-entering the prompt. ​
  • Example: "Generate names and job titles indefinitely using the template: https://myapi.com/NAME/profile/JOB. ​ Stop when I say 'stop'." ​

11. Visualization Generator Pattern ​

  • Purpose: Create textual inputs for visualization tools. ​
  • Example: "Whenever I ask you to visualize something, create either a Graphviz Dot file or DALL-E prompt based on the needs of the visualization." ​

12. Game Play Pattern ​

  • Purpose: Create a game around a specific topic. ​
  • Example: "We are going to play a cybersecurity game. ​ Pretend to be a Linux terminal for a compromised computer. ​ I will use commands to investigate the attack." ​

13. Reflection Pattern

  • Purpose: Explain the reasoning and assumptions behind answers. ​
  • Example: "When you provide an answer, explain the reasoning and assumptions behind your selection of software frameworks, using code samples to support your explanation." ​

14. Refusal Breaker Pattern

  • Purpose: Suggest alternative wordings when the LLM refuses to answer. ​
  • Example: "Whenever you can’t answer a question, explain why and provide one or more alternate wordings of the question." ​

15. Context Manager Pattern ​

  • Purpose: Specify or remove context for a conversation. ​
  • Example: "When analyzing the following code, only consider security aspects. ​ Ignore formatting or naming conventions." ​

16. Recipe Pattern

  • Purpose: Generate a sequence of steps to achieve a goal, filling in missing steps and identifying unnecessary ones. ​
  • Example: "I am trying to deploy an application to the cloud. ​ I know I need to install dependencies and sign up for an AWS account. ​ Provide a complete sequence of steps, fill in missing ones, and identify unnecessary ones." ​

These patterns provide structured ways to interact with LLMs for improved output and interaction. ​

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