In this module, we will explore how essential data is as the key ingredient in training LLMs. You’ll learn about tokenization, diverse datasets, and how high-quality data ensures a more accurate and effective model. Additionally, we’ll discuss the impact of biases and how they can be managed during the training process.

In this module, we will break down the step-by-step process of training LLMs at scale, from mixing data to fine-tuning the model. You’ll get an inside look at the hardware required to train these models efficiently and explore different training methods like pretraining and fine-tuning for optimal performance.

In this module, we will dive into the art of prompt engineering—how the right combination of instructions can lead to better responses from your LLM. You’ll learn about different prompting styles, how to craft the best prompts for specific tasks, and how to evaluate their effectiveness.

In this module, we will explore how fine-tuning transforms a general LLM into a specialized tool for specific applications. You’ll learn about transfer learning, techniques for efficient fine-tuning, and hands-on methods to apply fine-tuning on your own datasets.

In this module, we will focus on how to evaluate the quality of LLM outputs through both data-driven metrics and human feedback. We’ll also discuss how to detect and correct common model errors, like hallucinations, and how to ensure fairness by addressing bias in LLMs.

In this module, we will guide you through the deployment phase—taking your trained LLM from the lab to the real world. You’ll learn how to wrap your model in APIs, build interactive demos, and choose the best platforms for hosting and scaling your model for wide-reaching use.

In this module, we will explore how LLMs can power real-world applications, from chatbots to personalized recommendations. You’ll learn how to use no-code tools for rapid prototyping and even build your own fully functional LLM-powered app as a final project.

In this module, we will teach you how to ensure your LLM stays fresh and effective over time. You’ll learn how to collect and incorporate feedback, track model performance with monitoring tools, and counteract model drift to keep your application aligned with user needs.

In this final module, we will help you map out your career path in the rapidly growing field of LLM engineering. You’ll learn how to build a standout portfolio, contribute to open-source projects, and prepare for interviews, ensuring you're ready to take on leadership roles in the industry.