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Essential AI Prompting Techniques and Templates

AI prompting techniques are fundamental methods for getting optimal results from language models. Each technique serves different purposes and can significantly improve AI responses when used appropriately.

Core Prompting Techniques

Zero-Shot Prompting The simplest form of prompting that requires no examples. Simply state your request directly: 

Analyze the sentiment of this review: "The product exceeded my expectations but shipping was slow."

One-Shot Prompting Provides a single example to guide the AI's response format: 

Format: 
Blog Title: [Title]
Meta Title: [SEO Title]
Meta Description: [Description]

Please create content for the topic "artificial intelligence trends"

Few-Shot Prompting Uses multiple examples to guide the AI's understanding: 

Convert these sentences to past tense:
Input: I walk to the store
Output: I walked to the store

Input: She runs fast
Output: She ran fast

Input: They sing well
Output: [Your turn to complete]

Advanced Techniques

Chain-of-Thought Prompting Breaks down complex problems into logical steps: 

Let's solve this problem step by step:
1. First, understand the given information
2. Then, identify the key variables
3. Finally, calculate the solution

Problem: If a company has 50 employees and each generates $10,000 monthly revenue, what's the annual revenue?

Role-Based Prompting Assigns a specific persona to the AI: 

You are an experienced cybersecurity expert. Please analyze these network security practices and identify potential vulnerabilities:
[List practices here]

Template Prompts

Universal Enhancement Template 

Whenever I give you any instruction, you will:
1. Refine the instruction to improve clarity, specificity, and effectiveness.
2. Create a relevant perspective to adopt for interpreting the instruction.
3. Present the refined version of the instruction using the format 'Refined: [refined instruction]'.
4. State the perspective you'll adopt using the format 'Perspective: [chosen perspective]'.
5. Execute the refined instruction from the chosen perspective and present the result using the format 'Execution: [answer]'.

Iterative Improvement Template

Example 1: 

I want you to become my Prompt engineer. Your goal is to help me craft the best possible prompt for my needs. The prompt with be used by you <OpenAI, copilot, etc>.

You will follow the following process:

1. Your first response wil be to ask me what the prompt should be about. I will provide my answer, but we will need to improve it through continual iterations by going through the next steps.
2. Based on my input, you will generate 2 sections.
a. Revised prompt (provide you rewritten prompt. It should be clear, concise, and easily understood by you)
b. Questions (ask any relevant questions pertaining to what additional information is needed from me to improve the prompt)
3. We will continue this iterative process with me providing additional information to you and you updating the prompt in the Revised prompt section until I say we are done.

Example 2: 

Fully analyze the intentions of the following prompt I've made. Use everything you know about prompt engineering and my intentions, to improve the prompt to ensure that its output is always high quality and accurately satisfies the prompt's request. Please make sure to keep all the exact precise details of the prompt intact and just improve/perfect it to generate the best possible output. After your prompt output, in a separate section ask me 3 questions that will allow you to continue progressively improving on the prompt until I am satisfied.
"[Prompt here]"

Best Practices

  • Be specific and clear in your instructions
  • Provide relevant context and constraints
  • Use step-by-step reasoning for complex tasks
  • Include examples when format is important
  • Specify the desired output format
  • Break down complex requests into smaller components

Additional Tips

  • Start with simple prompts and iterate based on responses
  • Use role-playing for specialized knowledge
  • Combine multiple techniques for complex tasks
  • Include error handling instructions
  • Specify the target audience and purpose
  • Test different variations to find optimal results

Remember: The effectiveness of these techniques may vary depending on the AI model and specific use case. Regular experimentation and refinement will help determine the most effective approach for your needs.