Prompt Engineering and Few-Shot Fine-Tuning Training Course
Prompt Engineering and Few-Shot Fine-Tuning equips 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 a variety of tasks.
This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications in public sector environments.
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 natural language processing tasks.
- Leverage few-shot techniques to adapt LLMs with minimal data requirements.
- Optimize LLM performance for practical applications, specifically tailored for government use cases.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for government, please contact us to arrange.
Course Outline
Introduction to Prompt Engineering for Government
- What is prompt engineering?
- Importance of prompt design in large language models (LLMs)
- Comparison of zero-shot, one-shot, and few-shot approaches
Designing Effective Prompts for Government
- Principles of crafting high-quality prompts for government use
- Experimenting with prompt variations to meet public sector needs
- Common challenges in prompt design within the government context
Few-Shot Fine-Tuning for Government Applications
- Overview of few-shot learning and its relevance to government operations
- Applications in task-specific LLM adaptation for government workflows
- Integrating few-shot examples into prompts for enhanced performance
Hands-On with Prompt Engineering Tools for Government
- Using the OpenAI API for prompt experimentation in public sector projects
- Exploring prompt design with Hugging Face Transformers for government applications
- Evaluating the impact of prompt variations on government-specific tasks
Optimizing LLM Performance for Government
- Evaluating outputs and refining prompts to enhance accuracy and relevance
- Incorporating context for better results in public sector applications
- Handling ambiguities and bias in LLM responses to ensure fairness and transparency
Applications of Prompt Engineering for Government
- Text generation and summarization for government reports and communications
- Sentiment analysis and classification for public feedback and social media monitoring
- Creative writing and code generation for internal documentation and software development
Deploying Prompt-Based Solutions for Government
- Integrating prompts into government applications to streamline processes
- Monitoring performance and scalability to ensure robust deployment
- Case studies and real-world examples of successful prompt-based solutions in the public sector
Summary and Next Steps for Government
Requirements
- Basic understanding of natural language processing (NLP)
- Familiarity with Python programming
- Experience with large language models (LLMs) is beneficial
Audience for Government
- 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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