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Creating Artificial Intelligence (AI) Agents with ChatGPT Training Course

Coming Soon

Formats ⟩  Live Virtual: 4 hrs./1 Day  |  In-Person: 6 hrs./1 Day

Creating AI Agents with ChatGPT equips participants with the skills to design and deploy effective AI assistants using ChatGPT’s custom capabilities. Participants will learn how to structure agent behavior through clear instructions, prompt design, and defined constraints to ensure consistent, high-quality outputs. The course also covers integrating knowledge, refining performance through testing, and addressing key considerations such as bias, security, and responsible AI use. By the end, learners will be able to build AI agents that enhance productivity and support real-world workflows.

Learning Objectives

  • Understand how ChatGPT-based agents operate.
  • Learn how instructions shape agent behavior.
  • Define roles, goals, constraints, and interaction patterns.
  • Apply prompt engineering techniques.
  • Incorporate relevant knowledge and context to enhance responses.
  • Refine agent performance through testing and iteration.
  • Address risks related to bias, sensitivity, and responsible AI use.
  • Identify practical use cases for deploying AI agents.

Course Agenda »

Foundations of AI Agents in ChatGPT

  1. What Are AI Agents?
  2. How ChatGPT Generates Responses
  3. From Prompts to Persistent Agents
  4. Common Use Cases for AI Agents

Designing Effective Agents

  1. Defining Purpose and Scope
  2. Structuring Instructions and Roles
  3. Establishing Boundaries and Constraints
  4. Consistency and Reliability

Prompt Engineering and Interaction

  1. Principles of Effective Prompting
  2. Guiding Tone, Style, and Output Format
  3. Ambiguity and Misinterpretation
  4. Designing Multi-Step Interactions

Enhancing Knowledge and Accuracy

  1. Context and Reference Material
  2. Files and Inputs to Improve Responses
  3. Reducing Hallucinations and Errors
  4. Validating Outputs

Testing and Optimization

  1. Evaluating Agent Performance
  2. Iterating on Instructions and Prompts
  3. Debugging Common Issues
  4. Improving Usability and Adoption

Responsible AI and Deployment

  1. Managing Bias
  2. Ethical Considerations
  3. Data Privacy and Security Awareness
  4. Preparing Agents for Real-World Use
  5. Identifying Opportunities for Scale

Creating Artificial Intelligence (AI) Agents with ChatGPT Training Course

Coming Soon

Formats ⟩  Live Virtual: 4 hrs./1 Day  |  In-Person: 6 hrs./1 Day

Creating AI Agents with ChatGPT equips participants with the skills to design and deploy effective AI assistants using ChatGPT’s custom capabilities. Participants will learn how to structure agent behavior through clear instructions, prompt design, and defined constraints to ensure consistent, high-quality outputs. The course also covers integrating knowledge, refining performance through testing, and addressing key considerations such as bias, security, and responsible AI use. By the end, learners will be able to build AI agents that enhance productivity and support real-world workflows.

Learning Objectives

  • Understand how ChatGPT-based agents operate.
  • Learn how instructions shape agent behavior.
  • Define roles, goals, constraints, and interaction patterns.
  • Apply prompt engineering techniques.
  • Incorporate relevant knowledge and context to enhance responses.
  • Refine agent performance through testing and iteration.
  • Address risks related to bias, sensitivity, and responsible AI use.
  • Identify practical use cases for deploying AI agents.

Course Agenda »

Foundations of AI Agents in ChatGPT

  1. What Are AI Agents?
  2. How ChatGPT Generates Responses
  3. From Prompts to Persistent Agents
  4. Common Use Cases for AI Agents

Designing Effective Agents

  1. Defining Purpose and Scope
  2. Structuring Instructions and Roles
  3. Establishing Boundaries and Constraints
  4. Consistency and Reliability

Prompt Engineering and Interaction

  1. Principles of Effective Prompting
  2. Guiding Tone, Style, and Output Format
  3. Ambiguity and Misinterpretation
  4. Designing Multi-Step Interactions

Enhancing Knowledge and Accuracy

  1. Context and Reference Material
  2. Files and Inputs to Improve Responses
  3. Reducing Hallucinations and Errors
  4. Validating Outputs

Testing and Optimization

  1. Evaluating Agent Performance
  2. Iterating on Instructions and Prompts
  3. Debugging Common Issues
  4. Improving Usability and Adoption

Responsible AI and Deployment

  1. Managing Bias
  2. Ethical Considerations
  3. Data Privacy and Security Awareness
  4. Preparing Agents for Real-World Use
  5. Identifying Opportunities for Scale
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