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Duration 14 hours
Course Outline
Introduction to Prompt Engineering
- Defining prompt engineering.
- The role of prompt design in enhancing LLM capabilities.
- Comparing zero-shot, one-shot, and few-shot methodologies.
Crafting High-Quality Prompts
- Key principles for developing effective prompts.
- Testing different prompt variations.
- Addressing common pitfalls in prompt design.
Few-Shot Fine-Tuning
- Overview of few-shot learning concepts.
- Applying few-shot techniques for task-specific LLM adaptation.
- Integrating few-shot examples into prompt structures.
Hands-On with Prompt Engineering Tools
- Experimenting with prompts via the OpenAI API.
- Utilizing Hugging Face Transformers for prompt design.
- Assessing the effectiveness of different prompt variations.
Optimizing LLM Performance
- Evaluating model outputs and refining prompts accordingly.
- Leveraging context to improve result quality.
- Managing ambiguity and bias in LLM responses.
Applications of Prompt Engineering
- Text generation and summarization tasks.
- Sentiment analysis and text classification.
- Creative writing and code generation.
Deploying Prompt-Based Solutions
- Embedding prompts into application workflows.
- Monitoring performance metrics and scalability.
- Reviewing case studies and real-world implementations.
Summary and Next Steps
Requirements
- A foundational understanding of natural language processing (NLP).
- Proficiency in Python programming.
- Prior experience with large language models (LLMs) is advantageous but not mandatory.
Target Audience
- AI developers.
- NLP engineers.
- Machine learning practitioners.
