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Course Outline
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
- Core 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.
- Incorporating 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.
- Assessing the impact of prompt variations.
Optimizing LLM Performance
- Evaluating outputs and refining prompts.
- Incorporating context to improve results.
- Managing 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 knowledge of natural language processing (NLP).
- Familiarity with Python programming.
- Prior experience with large language models (LLMs) is advantageous.
Audience
- AI developers.
- NLP engineers.
- Machine learning practitioners.
14 Hours
