Course Outline

Introduction to Prompt Engineering

  • What is prompt engineering?
  • Importance of prompt design in LLMs
  • Comparison of zero-shot, one-shot, and few-shot approaches

Designing Effective Prompts

  • Principles of crafting high-quality prompts
  • Experimenting with prompt variations
  • Common challenges in prompt design

Few-Shot Fine-Tuning

  • Overview of few-shot learning
  • Applications in task-specific LLM adaptation
  • Integrating few-shot examples into prompts

Hands-On with Prompt Engineering Tools

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

Optimizing LLM Performance

  • Evaluating outputs and refining prompts
  • Incorporating context for better results
  • Handling 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

  • Basic understanding of natural language processing (NLP)
  • Familiarity with Python programming
  • Experience with large language models (LLMs) is a plus

Audience

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

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