This is an introductory-level microlearning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete.

You’ll learn Skills

1. Understanding of LLMs: You will learn the fundamentals of how large language models like GPT-3 work, including their architecture, training process, and the underlying principles of natural language processing (NLP).
2. Natural Language Processing (NLP): Courses on LLMs often cover NLP concepts such as tokenization, word embeddings, text preprocessing, and text classification, which are essential for working with text data.
3. Model Fine-Tuning: You may learn how to fine-tune pre-trained LLMs for specific tasks. Fine-tuning is crucial for adapting these models to perform tasks like text classification, text generation, or question-answering effectively.
4. Text Generation: You’ll likely gain skills in generating text using LLMs. This can include tasks like chatbot development, content generation, and creative writing.
5. Ethical Considerations: LLMs can be used to generate potentially harmful content. You may learn about the ethical considerations and guidelines for responsible use, including issues related to bias and fairness.
6. Deployment and Integration: Courses may cover how to deploy LLMs in real-world applications, including web applications, chatbots, or other AI-powered systems. This involves integrating the models with various technologies and frameworks.
7. Data Preparation: Effective data preprocessing and data cleaning are critical for training and fine-tuning LLMs. You may learn how to prepare and preprocess text data to improve model performance.
8. Hyperparameter Tuning: Tuning the hyperparameters of LLMs can significantly impact their performance. You may learn techniques for optimizing hyperparameters and model selection.
9. Evaluation Metrics: Understanding how to evaluate the performance of LLMs is crucial. You may learn about common evaluation metrics used in NLP tasks, such as accuracy, precision, recall, F1-score, and perplexity.
10. Advanced NLP Techniques: Depending on the course’s depth, you might delve into advanced NLP techniques such as named entity recognition, sentiment analysis, and language translation using LLMs.
11. Interpreting Model Outputs: LLMs can be challenging to interpret. You may learn techniques for interpreting model outputs and understanding how the model makes decisions.
12. Troubleshooting and Debugging: Debugging and troubleshooting issues that arise when working with LLMs is an important skill. You’ll learn how to diagnose and resolve common problems.
13. Model Comparison: You may explore different LLM architectures and understand when to choose one over another for specific tasks.
14. Transfer Learning: Transfer learning is a key concept in working with LLMs. You’ll learn how to leverage pre-trained models and transfer knowledge to new tasks.
15. Hands-on Projects: Many courses include hands-on projects where you apply what you’ve learned to real-world problems, gaining practical experience.

This module explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

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