ChatGPT: Define the Task for Better AI Interactions

Have you ever wondered why your prompts to AI don’t yield the desired results? Maybe you’re using ChatGPT inefficiently. How often do you ask yourself, “Why aren’t my prompts working as expected?” Let’s dive into how to correctly formulate tasks to get accurate and useful answers.

My colleague Jessica, a marketing manager, once complained to me about how her prompts to ChatGPT often resulted in overly general answers. She said, “I asked the AI to help with ideas for promoting a new product, and it gave me a bunch of generic advice I already knew!”

So, we decided to try a different approach. Instead of asking something vague like “How to improve marketing?”, Jessica started asking more specific questions, such as: “What are three strategies to increase Instagram reach among young adults?” The results were much better: the AI provided her with concrete and useful tips she could implement right away.

This highlights the importance of properly formulating your prompts to AI. In this article, we’ll show you how to create effective tasks for ChatGPT to get the most accurate and useful responses. Proper task definition is a key element of successful AI interactions. For more comprehensive insights into crafting effective prompts, be sure to read our main article Writing Effective ChatGPT Prompts: 7 Essential Rules, which covers the key principles for optimizing your AI communication.

Main Methods for Task Formulation

Prototyping

Prototyping is a method of improving prompt formulations through iterative testing and refinement. This approach allows you to experiment with different versions of prompts to find the most effective and precise formulation. The goal of prototyping is to turn an initially broad or unclear prompt into a specific and clear question that will yield more relevant and useful responses from the AI.

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How Prototyping Works

1. Start Broad:
   • Initial Prompt: “How to improve sales?”
     • Issue: Too general, leading to vague and generic advice.
2. Narrow the Scope:
   • Refined Prompt: “How to improve sales in an online store?”
     • Improvement: Limits the scope, making it easier for AI to provide relevant advice.
3. Add Specific Goals or Methods:
   • Further Refinement: “What marketing methods can help improve sales in an online clothing store?”
     • Improvement: More precise and focused on specific methods.
4. Targeted and Detailed:
   • Final Prompt: “What are three methods to increase sales in an online clothing store targeting young adults?”
     • Result: The AI now understands the context (online clothing store), the goal (increasing sales), and the target audience (young adults), significantly improving the accuracy and usefulness of the response.

Example:

• Broad Prompt: “How to improve employee productivity??”
• Narrowed Prompt: “What are three ways to improve employee productivity in an office setting?”
• More Specific: “What are three ways to improve employee productivity in an office setting with a focus on remote work?”
• Targeted and Detailed: “How can we improve employee productivity in an office setting with a focus on remote work and limited face-to-face interactions?”

Using the prototyping method helps progressively refine prompts, making them more specific and targeted. This not only saves time but also enhances the quality of AI interactions, ensuring you receive the most relevant responses.

Scenario-Based Task Definition

Scenario-based task definition is a method that helps AI better understand the context and goals of a prompt, providing more accurate and useful responses. This approach involves creating situations or roles that AI should consider when responding to the prompt. Including context, conditions, and constraints helps AI focus on specific aspects of the task.

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How Scenario-Based Task Definition Works

1. Create a Role and Context:
   • Initial Scenario: “Imagine you are a marketing manager at a large tech company. How would you use AI to improve the sales strategy?”
     • Benefit: Provides AI with a role and context, helping it generate more targeted recommendations.
2. Add Conditions and Constraints:
   • Detailed Scenario: “Imagine you are a marketing manager at a large tech company operating in the B2B market, and you have a limited budget. How would you use AI to improve the sales strategy with minimal costs?”
     • Improvement: Specific conditions make the prompt more precise, allowing AI to consider both goals and limitations.
3. Define Comprehensive Strategies:
   • Step-by-Step Scenario: “Imagine you are a marketing manager, and your goal is to increase sales by 20% over the next quarter. How would you use AI to analyze customer data, develop personalized marketing campaigns, and optimize advertising spend?”
     • Benefit: Helps AI propose a step-by-step action plan, considering all important aspects of the task.

Example:

• General Prompt: “How to improve the onboarding process for new employees?”
• Scenario-Based Prompt: “Imagine you are an HR manager at a mid-sized tech company. How would you use AI to improve the onboarding process for new employees?”
• Detailed Scenario: “Imagine you are an HR manager at a mid-sized tech company with a high turnover rate. How would you use AI to improve the onboarding process for new employees, focusing on reducing turnover?”
• Comprehensive Strategy Scenario: “Imagine you are an HR manager, and your goal is to reduce the turnover rate by 15% within the next year. How would you use AI to streamline the onboarding process, enhance employee engagement from day one, and provide personalized training plans?”

Using scenario-based task definition allows for more detailed and focused prompts. This increases the relevance and practical applicability of AI responses, helping achieve set goals while considering specific conditions and constraints.

Task Decomposition

Method Explanation

Task decomposition is the process of breaking down a complex task into smaller, manageable parts. This method allows for a better understanding of the problem’s structure, identifies key elements, and enables sequential resolution of each part. This approach is particularly useful when a task is too broad or abstract, making it difficult to address as a whole.

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Example 1: Improving Customer Service

Consider the task “How to improve customer service?” This task is too general and doesn’t provide a clear direction for action. Let’s start by decomposing it:

• First Level Decomposition: Identify key aspects of customer service that can be improved:
  • Response time.
  • Quality of support.
  • Customer satisfaction.
• Second Level Decomposition: Break down each aspect into smaller tasks. For example, to improve response time:
  • What methods can be used to improve response time in customer support?
  • What automation tools can reduce response time?

Example 2: Customer Satisfaction

Continue decomposing for the aspect of customer satisfaction:

• First Level Decomposition: Identify key elements that affect satisfaction:
  • Speed of problem resolution.
  • Quality of solutions.
  • Ease of interaction with support.
• Second Level Decomposition: Break down each element into specific tasks. For example, for speed of problem resolution:
  • How can the information transfer process between departments be improved to speed up problem resolution?
  • What technologies can help automate prompt processing?

Principle of Decomposition

The main principle of decomposition is to ask specific questions about each aspect of the task and sequentially break them down into smaller parts until clear, concrete tasks remain that can be addressed individually. This helps systematize the approach to solving complex problems and makes the process more manageable.

Use Case Modeling

Use case modeling is a method that allows creating specific scenarios for applying AI in various situations. This approach helps to better understand how AI can solve a particular problem and what results to expect. The primary goal of use case modeling is to precisely define the requirements and expectations from AI, minimizing the likelihood of receiving irrelevant or suboptimal responses.

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How Use Case Modeling Works

1. Define the Scenario:
   • Example: “Describe how AI can assist in managing inventory for retail stores.”
     • Explanation: AI can use historical sales data, current trends, and other external factors to forecast product demand, helping retail stores optimize inventory, reduce storage costs, and minimize stockouts or overstocking.
2. Specify Details for Clarity:
   • General Prompt: “How can AI help in inventory management?”
   • Refined Prompt: “How can AI use historical sales data and current market trends to forecast demand and optimize inventory in retail?”
     • Improvement: Specifies details that help AI understand the context better and provide more accurate solutions.
3. Consider the Task’s End Goal:
   • Example for Customer Service: “How can AI use data from previous customer interactions to personalize service and increase customer satisfaction?”
     • Explanation: AI can analyze data on purchases, customer support interactions, and feedback to offer personalized recommendations and anticipate customer needs.

Examples:

• Inventory Management:
  • General Prompt: “How can AI help in inventory management?”
  • Refined Prompt: “How can AI use historical sales data and current market trends to forecast demand and optimize inventory in retail?”
    • Result: AI understands the context and specifics, leading to better inventory management solutions.
• Customer Service:
  • General Prompt: “How can AI improve customer service?”
  • Refined Prompt: “How can AI use data from previous customer interactions to personalize service and increase customer satisfaction?”
    • Result: AI focuses on analyzing customer data to offer personalized services, improving customer satisfaction.

Key Considerations

• Task’s End Goal:
  • Sales Increase: AI should analyze purchasing habits and predict trends.
  • Customer Service Improvement: Emphasis should be on personalizing interactions and analyzing feedback.
• Identifying Potential Problems:
  • Use case modeling helps identify potential issues and bottlenecks, preparing you for challenges and developing strategies to overcome them.

By using use case modeling, you can better understand how AI can assist in solving specific tasks and identify potential problems in advance. This method ensures more effective and predictable AI applications, ultimately leading to better outcomes.

Interactive Testing

Interactive Testing is a method where you continuously test and refine your prompts to improve results. This approach allows you to adapt and optimize interactions with AI, ensuring more accurate and useful responses.

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How Interactive Testing Works

1. Start with a Basic Prompt:
   • Initial Prompt: “How to improve customer service in a restaurant?”
     • Issue: Too broad, may lead to vague and impractical answers.
2. First Iteration:
   • Refined Prompt: “What are three ways to improve customer service in a restaurant during evening hours?”
     • Improvement: More specific, but may still be too general.
3. Add Context and Constraints:
   • Next Iteration: “What are three ways to improve customer service in a restaurant during peak evening hours with a limited number of staff?”
     • Improvement: Adds context and constraints for more relevant advice.
4. Focus on Specific Aspects:
   • Further Refinement: “How can we speed up customer service in a restaurant during peak evening hours with limited waitstaff?”
     • Result: Targeted and focused on specific issues, leading to more useful responses.

Example:

• Basic Prompt: “How to reduce energy consumption in a household?”
• First Iteration: “What are three ways to reduce energy consumption in a household during winter?”
• Next Iteration: “What are three ways to reduce energy consumption in a household during winter without sacrificing comfort?”
• Further Refinement: “How can we reduce energy consumption in a household during winter by optimizing heating systems and using smart home devices?”

Benefits of Interactive Testing

This interactive testing process allows you to gradually refine and improve your prompts, ultimately leading to better results. The advantage of this method is that you can adapt and adjust your prompts in real-time, responding to the results and obtaining the most accurate and useful answers.

Suitable Scenarios:

• When initial prompts do not yield desired results.
• For complex tasks requiring a flexible approach.

Interactive testing helps you find optimal formulations through iterations, making your interactions with AI more productive and effective.

Algorithm for Choosing the Right Method

Why is this Important?

Imagine you’re sending ChatGPT to the store to get some groceries. You tell it, “Get something tasty.” What happens? ChatGPT comes back with bags of gummy bears, popcorn, and maybe even some exotic fruits you’ve never heard of. It’s possible that “tasty” for ChatGPT means seaweed or even dried crickets! And all you wanted was some good old chocolate chip cookies. That’s why it’s essential to formulate your tasks clearly.

Defining the Task

1. Describe the Problem or Goal
   • Start with a clear description of the problem or goal you want AI to solve. This should be a specific and measurable statement.
2. Examples of Tasks
   • Increasing user engagement on a website.
   • Optimizing logistics for product delivery.
   • Improving customer service quality in a contact center.

Choosing the Method

Criteria for Selecting a Method:

• Prototyping: Ideal for experimenting and improving prompt formulations.
  • Example: “How to increase user engagement on the website?”
  • Prototyping prompts: from “How to increase engagement?” to “What are three strategies to increase interactions on our site among teenagers?”
• Scenarios: Used for creating context-based and role-specific tasks.
  • Example: “Optimizing logistics for product delivery.”
  • Scenario: “Imagine you are a logistics manager at a large retail company. How would you use AI to reduce delivery times and optimize routes?”
• Decomposition: Applied to break down complex and multi-level tasks.
  • Example: “Improving customer service quality in a contact center.”
  • Decomposition: Break the task into sub-tasks: “How to reduce waiting time?”, “What methods can increase customer satisfaction?”, “How to automate responses to frequently asked questions?”
• Modeling: Suitable for specific use cases.
  • Example: “How can AI help with inventory management in a warehouse?”
  • Modeling: Creating a use case model for AI to predict demand and optimize stock levels.
• Testing: Used for continuous improvement and verification of prompts.
  • Example: “How to improve the hiring process?”
  • Testing: Formulating and testing prompts, such as from “How to find the best candidates?” to “What online tools can help find the most qualified candidates for IT positions?”

Examples of the Algorithm

1. Defining the Task
   • Example: “How to improve sales forecast accuracy?”
2. Choosing the Method
   • Prototyping: “What factors most influence sales forecast accuracy?”
   • Scenarios: “Imagine you are a sales manager at a company, how would you use AI to improve sales forecasts?”
   • Decomposition: Break it down into sub-tasks: “How can AI help analyze historical data?”, “What forecasting methods are most effective?”
   • Modeling: “Create a use case for AI to analyze data and predict sales.”
   • Testing: Formulating and testing various prompts to improve forecast accuracy.
3. Applying the Chosen Method
   • Depending on the specific task and context, choose the appropriate method and follow the steps to implement it. For example, for improving sales forecast accuracy, modeling and testing might be the best fit.
4. Continuous Testing and Improvement
   • Continuously test and refine prompts at each stage to achieve the best results.

Examples from Different Fields:

• Marketing: “How to increase the effectiveness of advertising campaigns?” — Prototyping.
• Healthcare: “How can AI assist in diagnosing diseases?” — Modeling.
• Education: “What methods can improve student performance?” — Decomposition.
• Finance: “How can AI optimize investment strategies?” — Scenarios.
• Technology: “What approaches can improve software development?” — Testing.

By using this algorithm, you can effectively choose and apply task formulation methods for AI, significantly improving the quality and relevance of the responses you receive.

Downloadable Materials and Instruments for ChatGPT Task Formulation

Explore our specially curated resources to elevate your prompt engineering:

1. Cheat Sheet: Define the Task for Better AI Interaction: Master prompt writing with these crucial rules and expanded examples.
2. Free Prompt Checker Tool: Analyze and improve your prompts using CustomGPT technology.
3. Premium ChatGPT Prompt Generator: Advanced tool for rewriting and crafting new prompts based on this article.

FAQ: ChatGPT Task Formulation for Optimal Results

How-To: Choosing the Right Method for ChatGPT Tasks in 4 Steps

Conclusion

In conclusion, effectively defining tasks for ChatGPT and other AI systems is crucial for obtaining precise and relevant responses. By employing methods such as prototyping, scenario-based task definition, task decomposition, use case modeling, and interactive testing, you can significantly enhance the clarity and specificity of your prompts. This not only improves the quality of the information you receive but also ensures that your AI interactions are productive and aligned with your goals. Remember, the key to successful AI engagement lies in how well you articulate your requests.

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