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.

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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

14 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.