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 Duration 7 hours

Course Outline

Foundations of Prompt Engineering

  • Defining prompt engineering and understanding its importance
  • Exploring common use cases and their effect on productivity
  • An overview of typical model behaviors

Key Principles for Effective Prompts

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

Prompt Patterns and Reusable Templates

  • Implementing instruction-based and role-based prompts
  • Applying chain-of-thought and step-by-step prompting techniques
  • Utilizing few-shot examples and reusing templates

Practical Prompting Exercises

  • Developing prompts for summarization and rewriting tasks
  • Creating prompts for classification and data extraction
  • Live refinement: adjusting prompts based on observed outputs

Assessment and Optimization of Prompts

  • Utilizing metrics and heuristics to gauge prompt quality
  • Validating prompts through testing and edge case analysis
  • Managing version control and documentation for prompt changes

Safety, Bias, and Responsible Usage

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

Conclusion, Resources, and Future Directions

  • Accessing quick-reference templates and cheat sheets
  • Exploring recommended literature and community resources
  • Suggestions for ongoing practice and advanced learning paths

Requirements

  • Experience with web-based AI chat interfaces
  • A foundational grasp of natural language concepts
  • Readiness for iterative problem-solving approaches

Target Audience

  • Novices aiming to learn effective communication strategies with AI models
  • Product managers, content creators, and analysts exploring the capabilities of AI tools
  • Individuals tasked with creating or assessing AI-generated content

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