Prompt Engineering and Few-Shot Fine-Tuning Training Course

Introduction to Prompt Engineering

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

Designing Effective Prompts

• Core 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 methodologies
• Applications in task-specific LLM adaptation
• Integrating few-shot examples into prompts

Hands-On with Prompt Engineering Tools

• Utilizing the OpenAI API for prompt experimentation
• Exploring prompt design with Hugging Face Transformers
• Evaluating the impact of varying prompt structures

Optimizing LLM Performance

• Assessing outputs and refining prompts
• Incorporating context to enhance 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