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Duration 14 hours (2 days)
Course Outline
Introduction to Prompt Engineering
- What is prompt engineering?
- The importance of prompt design in LLMs
- Comparison of zero-shot, one-shot, and few-shot approaches
Designing Effective Prompts
- Principles for crafting high-quality prompts
- Experimenting with various prompt iterations
- Common challenges encountered in prompt design
Few-Shot Fine-Tuning
- Overview of few-shot learning
- Applications in adapting LLMs to specific tasks
- 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 different prompt variations
Optimizing 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 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
Requirements
- Basic understanding of natural language processing (NLP)
- Familiarity with Python programming
- Prior experience with large language models (LLMs) is advantageous
Target Audience
- AI developers
- NLP engineers
- Machine learning practitioners