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 Duration 7 hours

Course Outline

Introduction to Prompt Engineering

  • Defining prompt engineering and its importance
  • Common use cases and their impact on productivity
  • An overview of typical model behaviors

Key Principles of Effective Prompts

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

Prompt Patterns and Templates

  • Instruction-based and role-based prompts
  • Chain-of-thought and step-by-step prompting techniques
  • Few-shot examples and reusing templates

Practical Prompting Exercises

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

Assessing and Enhancing Prompts

  • Metrics and heuristics for evaluating prompt quality
  • Validating prompts using tests and edge cases
  • Versioning and documenting prompt modifications

Safety, Bias, and Responsible Usage

  • Identifying and mitigating biased or unsafe outputs
  • Implementing basic guardrails and content constraints
  • Determining when human review is necessary

Conclusion, Resources, and Next Steps

  • Quick-reference templates and cheat sheets
  • Recommended reading materials and community resources
  • Guidance for continued practice and learning pathways

Requirements

  • Familiarity with web-based AI chat interfaces
  • A basic grasp of natural language concepts
  • Experience with iterative problem-solving approaches

Target Audience

  • Novices seeking to communicate effectively with AI models
  • Product managers, content creators, and analysts exploring AI tools
  • Individuals responsible for generating or assessing AI-generated content

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