Prompt Engineering and Few-Shot Fine-Tuning Training Course

Introduction to Prompt Engineering

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

Designing Effective Prompts

• Key principles for creating high-quality prompts.
• Testing and experimenting with different prompt variations.
• Addressing common challenges in prompt design.

Few-Shot Fine-Tuning

• An overview of few-shot learning concepts.
• Applications in task-specific LLM adaptation.
• Integrating few-shot examples into prompt structures.

Hands-On with Prompt Engineering Tools

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

Optimizing LLM Performance

• Evaluating outputs and refining prompts for better accuracy.
• Incorporating context to improve model responses.
• Managing ambiguities and potential bias in LLM outputs.

Applications of Prompt Engineering

• Text generation and summarization tasks.
• Sentiment analysis and classification.
• Creative writing and code generation.

Deploying Prompt-Based Solutions

• Integrating prompts into larger applications.
• Monitoring performance and ensuring scalability.
• Reviewing case studies and real-world implementations.

Summary and Next Steps