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Course Outline

Overview of Prompt Engineering Concepts

  • Definition and scope of prompt engineering
  • Critical role of prompt design in large language model (LLM) functionality
  • Comparison of zero-shot, one-shot, and few-shot methodologies for government use cases

Strategies for Developing Effective Prompts

  • Fundamental principles for constructing high-fidelity prompts
  • Methodologies for testing prompt variations
  • Identification of common obstacles in prompt development

Implementation of Few-Shot Learning Techniques

  • Conceptual overview of few-shot learning frameworks
  • Application to task-specific LLM adaptation for public sector needs
  • Procedures for integrating illustrative examples into prompt structures

Practical Application of Prompt Engineering Tools

  • Utilization of the OpenAI API for experimental prompt development
  • Exploration of prompt design capabilities via Hugging Face Transformers
  • Assessment of the impact associated with prompt variations

Enhancement of Large Language Model Performance

  • Evaluation methodologies for outputs and iterative prompt refinement
  • Incorporation of contextual data to improve response accuracy
  • Mitigation strategies for ambiguities and bias in model responses

Operational Applications of Prompt Engineering

  • Automation of text generation and summarization processes
  • Execution of sentiment analysis and classification tasks
  • Support for creative writing initiatives and code generation workflows

Deployment of Prompt-Based Solutions in Government Systems

  • Integration of prompt mechanisms into existing application infrastructures
  • Monitoring protocols for performance metrics and scalability requirements
  • Review of case studies and real-world implementation examples

Executive Summary and Strategic Next Steps

Requirements

  • Foundational knowledge of natural language processing (NLP) techniques
  • Competency in Python programming
  • Prior experience with large language models (LLMs) is advantageous for candidates seeking roles tailored for government applications

Audience

  • Artificial intelligence developers
  • Natural language processing engineers
  • Machine learning professionals
 14 Hours

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