Course Outline

Introduction to Prompt Engineering for Government

  • What is prompt engineering?
  • Importance of prompt design in language models for government
  • Comparison of zero-shot, one-shot, and few-shot approaches for government applications

Designing Effective Prompts for Government

  • Principles of crafting high-quality prompts for government use
  • Experimenting with prompt variations to meet specific agency needs
  • Common challenges in prompt design for government operations

Few-Shot Fine-Tuning for Government Applications

  • Overview of few-shot learning for government tasks
  • Applications in task-specific language model adaptation for government
  • Integrating few-shot examples into prompts for enhanced performance

Hands-On with Prompt Engineering Tools for Government

  • Using the OpenAI API for prompt experimentation in government projects
  • Exploring prompt design with Hugging Face Transformers for government applications
  • Evaluating the impact of prompt variations on government-specific tasks

Optimizing Language Model Performance for Government

  • Evaluating outputs and refining prompts to improve government services
  • Incorporating context for better results in government operations
  • Handling ambiguities and bias in language model responses for government use

Applications of Prompt Engineering for Government

  • Text generation and summarization for government reports and communications
  • Sentiment analysis and classification for public feedback and policy evaluation
  • Creative writing and code generation for enhancing government workflows

Deploying Prompt-Based Solutions in Government

  • Integrating prompts into government applications to enhance efficiency and accuracy
  • Monitoring performance and scalability of prompt-based systems in government operations
  • Case studies and real-world examples of prompt engineering in government agencies

Summary and Next Steps for Government

Requirements

  • A foundational understanding of natural language processing (NLP)
  • Familiarity with Python programming
  • Prior experience with large language models (LLMs) is beneficial

Audience

  • AI developers for government and private sectors
  • NLP engineers
  • Machine learning practitioners
 14 Hours

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