Prompt Engineering and Few-Shot Fine-Tuning Training Course
Prompt Engineering and Few-Shot Fine-Tuning provides participants with practical knowledge of using prompt engineering techniques and few-shot learning to effectively guide large language models (LLMs). The course emphasizes achieving optimal results without extensive fine-tuning, enabling the efficient adaptation of pre-trained models for diverse tasks.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.
By the end of this training, participants will be able to:
- Understand the principles of prompt engineering and few-shot learning.
- Design effective prompts for various NLP tasks.
- Leverage few-shot techniques to adapt LLMs with minimal data.
- Optimize LLM performance for practical applications.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Prompt Engineering
- What is prompt engineering?
- Importance 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 prompt variations
- Common challenges 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 OpenAI API for prompt experimentation
- Exploring prompt design with Hugging Face Transformers
- Evaluating the impact of prompt variations
Optimizing LLM Performance
- Evaluating outputs and refining prompts
- Incorporating context for better results
- Handling 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
- Experience with large language models (LLMs) is a plus
Audience
- AI developers
- NLP engineers
- Machine learning practitioners
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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