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 Duration 7 hours

Course Outline

Introduction to Prompt Engineering

  • Defining prompt engineering and its importance
  • Common use cases and their effect on productivity
  • A general view of typical model behaviors

Fundamental Principles of Effective Prompts

  • Emphasis on clarity, context, constraints, and examples
  • Managing output length, format, and tone
  • Identifying common mistakes and strategies to prevent them

Prompt Patterns and Templates

  • Instruction-driven prompts and role-based prompting
  • Chain-of-thought reasoning and step-by-step prompting techniques
  • Few-shot learning examples and the reuse of templates

Practical Prompting Exercises

  • Developing prompts for text summarization and rewriting
  • Building prompts for data classification and extraction tasks
  • Live refinement: adjusting prompts based on generated outputs

Evaluating and Enhancing Prompts

  • Key metrics and heuristics for assessing prompt quality
  • Utilizing tests and edge cases to validate prompt effectiveness
  • Version control and documentation of prompt modifications

Safety, Bias, and Responsible Use

  • Detecting and mitigating biased or unsafe outputs
  • Implementing basic guardrails and content limitations
  • Determining when human oversight is necessary

Conclusion, Resources, and Future Directions

  • Quick-reference templates and cheat sheets
  • Recommended reading materials and community resources
  • Recommendations for ongoing practice and further learning paths

Requirements

  • Proficiency with web-based AI chat interfaces
  • A fundamental grasp of natural language concepts
  • An aptitude for iterative problem-solving

Target Audience

  • Novices aiming to master effective communication with AI models
  • Product managers, content creators, and analysts investigating AI tools
  • Individuals tasked with generating or evaluating AI-driven content

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