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 Duration 7 hours

Course Outline

Overview of Prompt Engineering

  • Defining prompt engineering and its importance
  • Common use cases and their impact on productivity
  • Understanding typical behaviors of language models

Fundamental Principles of Effective Prompting

  • The role of clarity, context, constraints, and examples
  • Managing output length, structure, and tone
  • Identifying common mistakes and strategies to avoid them

Prompt Patterns and Templates

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

Practical Prompting Exercises

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

Assessing and Enhancing Prompts

  • Metrics and heuristics for evaluating prompt effectiveness
  • Utilizing tests and edge cases to verify prompt robustness
  • Managing version control and documenting prompt evolution

Safety, Bias, and Responsible Usage

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

Conclusion, Resources, and Future Steps

  • Access to quick-reference templates and cheat sheets
  • Curated reading materials and community resources
  • Recommendations for ongoing practice and advanced learning paths

Requirements

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

Target Audience

  • Novices seeking to learn how to interact effectively with AI models
  • Product managers, content creators, and analysts integrating AI tools into their workflows
  • Individuals accountable for generating or assessing AI-produced content

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