Prompt Engineering and Few-Shot Fine-Tuning Training Course
Prompt Engineering and Few-Shot Fine-Tuning equips participants with practical insights into leveraging prompt engineering techniques and few-shot learning to effectively steer large language models (LLMs). The course focuses on achieving optimal outcomes without extensive fine-tuning, allowing for the efficient adaptation of pre-trained models across a wide range of tasks.
This instructor-led, live training (available online or onsite) targets intermediate-level professionals keen to harness the power of prompt engineering and few-shot learning to enhance LLM performance in real-world applications.
Upon completing this training, participants will be able to:
- Grasp the core principles of prompt engineering and few-shot learning.
- Create effective prompts tailored to various NLP tasks.
- Utilise few-shot techniques to adapt LLMs with minimal data requirements.
- Optimise LLM performance for practical, everyday applications.
Course Format
- Interactive lectures and discussions.
- Ample exercises and practice opportunities.
- Hands-on implementation within a live lab environment.
Course Customisation Options
- To request a customised training course, please get in touch with us to make arrangements.
Course Outline
Introduction to Prompt Engineering
- What constitutes prompt engineering?
- The significance 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 different prompt variations.
- Common challenges encountered 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 the OpenAI API for prompt experimentation.
- Exploring prompt design with Hugging Face Transformers.
- Evaluating the impact of various prompt modifications.
Optimising LLM Performance
- Assessing outputs and refining prompts.
- Incorporating context to improve results.
- Addressing ambiguities and bias in LLM responses.
Applications of Prompt Engineering
- Text generation and summarisation.
- 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.
- Prior experience with large language models (LLMs) is advantageous.
Target Audience
- AI developers.
- NLP engineers.
- Machine learning practitioners.
Open Training Courses require 5+ participants.
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Provisional Upcoming Courses (Require 5+ participants)
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