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Duration 14 hours
Course Outline
Introduction to Prompt Engineering
- Defining prompt engineering
- The significance of prompt design in LLMs
- Differentiating between zero-shot, one-shot, and few-shot methodologies
Creating Effective Prompts
- Core principles for developing high-quality prompts
- Testing various prompt modifications
- Addressing common obstacles in prompt design
Few-Shot Fine-Tuning
- Introduction to few-shot learning concepts
- Use cases in task-specific LLM adaptation
- Incorporating few-shot examples into prompt structures
Practical Work with Prompt Engineering Tools
- Leveraging the OpenAI API for prompt testing
- Investigating prompt design using Hugging Face Transformers
- Assessing the effect of different prompt variations
Enhancing LLM Performance
- Analyzing outputs and refining prompt strategies
- Adding context to improve model results
- Managing ambiguity and bias in LLM outputs
Use Cases for Prompt Engineering
- Text creation and summarization tasks
- Sentiment analysis and text classification
- Creative writing and code generation
Implementing Prompt-Based Solutions
- Embedding prompts into application workflows
- Monitoring system performance and scalability
- Reviewing case studies and practical examples
Recap and Future Directions
Requirements
- A foundational understanding of natural language processing (NLP)
- Proficiency in Python programming
- Prior experience with large language models (LLMs) is advantageous
Target Audience
- AI developers
- NLP engineers
- Machine learning practitioners
