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Course Outline

Introduction to Prompt Engineering

  • Defining prompt engineering.
  • The significance of prompt design in LLMs.
  • Comparing zero-shot, one-shot, and few-shot approaches.

Designing Effective Prompts

  • Principles for crafting 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.
  • Integrating few-shot examples into prompts.

Hands-On with Prompt Engineering Tools

  • Using the 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 improved 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

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

Audience

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

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