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

• Principles for developing high-quality prompts
• Experimenting with diverse prompt variations
• Common challenges encountered in prompt design

Few-Shot Fine-Tuning

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

Hands-On Experience with Prompt Engineering Tools

• Utilizing the OpenAI API for prompt experimentation
• Exploring prompt design using Hugging Face Transformers
• Evaluating the impact of different prompt variations

Optimizing LLM Performance

• Assessing outputs and refining prompts
• Incorporating context to improve results
• Addressing ambiguities and bias in LLM responses

Practical 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