Fine-Tuning Large Language Models: Maximizing Value and Performance for Custom AI Solutions Training

In this AI Solutions course, participants learn how to fine-tune large language models (LLMs) like Chat-GPT to build custom AI solutions tailored to specific use cases and domains. This course covers fine-tuning fundamentals, including data preparation, model selection, and training best practices. Participants will also learn how to evaluate and optimize fine-tuned models for improved performance, fairness, and safety. 

By focusing on the value and use cases of fine-tuned large language models, this course will empower participants to harness the potential of state-of-the-art AI technology for a wide range of applications.

Course Details


2 days

Skills Gained

  • Understand the principles and benefits of fine-tuning large language models like Chat-GPT
  • Prepare data sets and choose appropriate models for fine-tuning tasks
  • Implement best practices for training and optimizing fine-tuned models
  • Evaluate model performance, fairness, and safety in custom AI applications
  • Apply fine-tuning techniques to create AI solutions for various use cases and domain


  • Strong understanding of AI and machine learning concepts
  • Familiarity with natural language processing (NLP) techniques and tools
  • Experience in Python programming and working knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch)

Target Audience

  • Data scientists, AI/ML engineers, software developers, and professionals interested in developing custom AI applications using large language models like Chat-GPT.
Course Outline
  • Introduction to Large Language Models and Fine-Tuning
    • Overview of large language models (e.g., GPT-3, Chat-GPT)
    • Benefits and challenges of fine-tuning
    • Introduction to fine-tuning techniques and tools
  • Data Preparation and Model Selection
    • Principles of data selection and annotation for fine-tuning
    • Techniques for data preprocessing and cleaning
    • Criteria for selecting base models and architectures
  • Training and Optimizing Fine-Tuned Models
    • Best practices for training and hyperparameter tuning
    • Techniques for model optimization and regularization
    • Monitoring model convergence and addressing overfitting
  • Evaluating Model Performance, Fairness, and Safety
    • Metrics and techniques for model evaluation
    • Identifying and mitigating biases in fine-tuned models
    • Ensuring content safety and adherence to ethical guidelines
  • Fine-Tuning for Various Use Cases and Domains
    • Customizing AI solutions for content generation, sentiment analysis, customer service, and more
    • Adapting fine-tuning techniques for domain-specific applications
  • Capstone Project
    • Participants will apply the concepts and techniques learned throughout the course to fine-tune a large language model for a custom AI solution addressing a real-world challenge or opportunity
    • Presentation and discussion of capstone projects