Prompt Engineering and Few-Shot Fine-Tuning Training Course

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

• Guidelines for developing high-quality prompts
• Experimenting with different prompt structures
• Common challenges encountered in prompt design

Few-Shot Fine-Tuning

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

Practical Work with Prompt Engineering Tools

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

Optimizing LLM Performance

• Evaluating outputs and refining prompts
• Leveraging 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