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

• Principles of crafting high-quality prompts.
• Experimenting with different prompt variations.
• Common challenges encountered 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 various prompt modifications.

Optimising LLM Performance

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

Applications of Prompt Engineering

• Text generation and summarisation.
• 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