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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
  • An overview of typical model behaviors

Key Principles for Effective Prompts

  • Clarity, context, constraints, and illustrative examples
  • Managing output length, format, and tone
  • Identifying common pitfalls and strategies to avoid them

Prompt Patterns and Templates

  • Instruction-driven prompts and role assignment
  • Chain-of-thought reasoning and step-by-step structuring
  • Few-shot learning and template reusability

Practical Prompting Activities

  • Developing prompts for text summarization and rewriting
  • Creating prompts for data classification and extraction
  • Live iteration: Adjusting prompts based on model outputs

Assessing and Refining Prompts

  • Metrics and heuristics for evaluating prompt quality
  • Utilizing tests and edge cases for validation
  • Version control and documentation of prompt changes

Safety, Bias, and Responsible Usage

  • Identifying and mitigating biased or unsafe outputs
  • Implementing basic guardrails and content restrictions
  • Determining when human oversight is required

Conclusion, Resources, and Future Directions

  • Concise reference templates and quick guides
  • Curated reading materials and community resources
  • Recommendations for ongoing practice and learning trajectories

Requirements

  • Proficiency with web-based AI chat platforms
  • A foundational grasp of natural language principles
  • Ease with iterative problem-solving approaches

Target Audience

  • Novices seeking to interact effectively with AI models
  • Product managers, content specialists, and analysts investigating AI solutions
  • Professionals tasked with creating or assessing AI-generated content

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