Lesson 5: Mastering Advanced Prompting Techniques

In this chapter we dive deeper into advanced prompting techniques, providing more insights, tips, and practical examples to help you excel in prompt engineering.

This lesson is part of The Prompt Artisan Prompt Engineering in ChatGPT: A Comprehensive Master Course.

5.1. Conversational Context Manipulation

Mastering conversational context manipulation is crucial when working with ChatGPT, which is designed for interactive dialogue. By skillfully manipulating context, you can steer the conversation to achieve desired outcomes:

  • Reiterate or emphasize important information to keep the AI model focused on the topic.
  • Introduce new information or modify existing context to refine the AI model’s responses.
  • Craft follow-up questions to clarify, expand, or redirect the AI model’s responses.
  • An additional tip for conversational context manipulation is to manage expectations. Set boundaries and limitations for the AI model’s responses to maintain focus and relevance.

Initial Conversation:

"What are the benefits of exercising regularly?"

Manipulated Context:

"You mentioned the physical benefits of exercising regularly. Can you also explain the mental and emotional benefits?"
Examples of good and bad prompts:

Good Prompt:

"What are the top five renewable energy sources currently in use? Please focus on those with the highest global adoption rates." 

This prompt sets clear boundaries and maintains focus on the most relevant energy sources.

Bad Prompt:

"Tell me about renewable energy." 

This prompt is too broad and doesn’t manage the context effectively, which can lead to less focused responses.

5.2. Priming the Model with Examples

Priming the AI model with examples can improve the quality and relevance of its responses:

  • Provide one or more examples in the prompt to guide the AI model’s understanding of the desired output.
  • Ensure the examples are relevant, accurate, and unbiased to avoid leading the AI model astray.


Regular Prompt:

"Describe a healthy breakfast."

Primed Prompt:

"Describe a healthy breakfast, similar to oatmeal with fruits and nuts or a vegetable omelette with whole-grain toast."

Examples of good and bad prompts:

Good Prompt:

"Describe an innovative business idea in the field of sustainable fashion, such as a subscription-based clothing rental service or a zero-waste clothing line." 

This prompt effectively primes the model with relevant examples while still allowing for creativity.

Bad Prompt:

"Describe a sustainable fashion business just like Company X." 

This prompt overly restricts the model, limiting its ability to generate diverse and creative responses.

5.3. Debiasing Techniques

AI models like GPT and ChatGPT may unintentionally exhibit biases present in their training data. Implement debiasing techniques to minimize bias in generated outputs:

  • Acknowledge potential biases in the prompt and explicitly instruct the model to avoid them.
  • Request the model to consider multiple perspectives, fostering a more balanced and unbiased response.
  • Monitor output quality and regularly review the AI model’s responses to ensure that they remain unbiased and relevant.

Regular Prompt:

"What are some traditional gender roles in society?"

Debiased Prompt:

"Describe traditional gender roles in society, while ensuring your response is free from gender stereotypes and considers the evolving nature of these roles."

Always balance specificity and creativity. Provide enough guidance without stifling the AI model’s ability to generate diverse and creative responses.

Examples of good and bad prompts:

Good Prompt:

"Discuss the impact of automation on the job market, while providing a balanced view that considers both job displacement and the creation of new job opportunities." 

This prompt encourages an unbiased and balanced response by considering multiple perspectives.

Bad Prompt:

"Why are robots taking all our jobs?" 

This prompt introduces bias by assuming that robots are solely responsible for job displacement.

5.4. Conditional Instructions and Chaining

Conditional instructions and chaining enable you to create more complex and dynamic prompts:

  • Use conditional statements (e.g., “if,” “when,” “unless”) to specify different outcomes or actions based on certain conditions.
  • Chain instructions together to guide the AI model through a series of steps, tasks, or decisions.
  • Use logical operators: Incorporate operators like “and,” “or,” and “not” to create more complex and precise conditions.

Conditional Prompt:

"If the weather is sunny, suggest outdoor activities for the weekend; if it's rainy, suggest indoor activities."

Chained Prompt:

"First, list five renewable energy sources. Then, for each source, describe one major advantage and one potential drawback."

Examples of good and bad prompts:

Good Conditional Prompt:

"If a person is vegan and allergic to nuts, suggest three healthy snack options." 

This prompt uses logical operators to set specific conditions for the AI model’s response.

Bad Conditional Prompt:

"What should a vegan eat?" 

This prompt lacks conditional instructions, resulting in a less focused and potentially less useful response.

Good Chained Prompt:

"Create a list of three pros and three cons of electric vehicles, and then rank them based on their impact on consumers and the environment." 

This prompt effectively chains instructions to guide the AI model through a structured process.

Bad Chained Prompt:

"Tell me about electric cars and the environment." 

This prompt doesn’t provide enough structure or guidance for the AI model to generate a focused, coherent response.

Mastering advanced prompting techniques is essential for prompt engineering experts. By incorporating these techniques into your toolkit, you’ll be able to craft prompts that fully harness the capabilities of AI language models like GPT and ChatGPT, delivering exceptional results in even the most challenging applications.

By now your skills are certainly improved. You are ready to go on the next lesson Lesson 6: Evaluating and Testing Prompts.

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