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Course Outline

Introduction to Prompt Engineering

  • What constitutes prompt engineering?
  • The significance of prompt design in LLMs
  • Comparing zero-shot, one-shot, and few-shot methodologies

Crafting Effective Prompts

  • Key principles for developing high-quality prompts
  • Experimenting with different prompt variations
  • Common challenges encountered in prompt design

Few-Shot Fine-Tuning

  • Overview of few-shot learning
  • Applications in adapting LLMs for specific tasks
  • Incorporating few-shot examples into prompts

Practical Application of Prompt Engineering Tools

  • Using the OpenAI API for prompt experimentation
  • Exploring prompt design via Hugging Face Transformers
  • Assessing the impact of prompt variations

Optimizing LLM Performance

  • Evaluating outputs and refining prompts
  • Integrating context to improve results
  • Addressing ambiguities and bias in LLM responses

Applications of Prompt Engineering

  • Text generation and summarization
  • Sentiment analysis and classification
  • Creative writing and code generation

Deploying Prompt-Based Solutions

  • Integrating prompts into applications
  • Monitoring performance and scalability
  • Case studies and real-world examples

Summary and Next Steps

Requirements

  • Foundational knowledge of natural language processing (NLP)
  • Proficiency in Python programming
  • Prior experience with large language models (LLMs) is advantageous

Target Audience

  • AI developers
  • NLP engineers
  • Machine learning practitioners
 14 Hours

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