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 approaches.

Designing Effective Prompts

• Key principles for crafting high-quality prompts.
• Experimenting with different prompt variations.
• Addressing common challenges in prompt design.

Few-Shot Fine-Tuning

• Overview of few-shot learning.
• Applications in task-specific LLM adaptation.
• 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 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