The basics of LLMs include understanding neural networks, transformers, large datasets, and the processes of pre-training and fine-tuning to enable the model to generate human-like text.
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Essentials of Large Language Models: A Beginner’s Journey
Learn how large language models work, from inference and training to prompting, embeddings, and RAG. Build practical skills to apply LLMs effectively in real-world language applications.
4.6
19 Lessons
2h
Updated 2 weeks ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of language models and large language models, including their capabilities, applications, and limitations
- Familiarity with the inference journey of an LLM, including tokenization, embeddings, positional encodings, and attention mechanisms
- Working knowledge of how LLMs are trained for next-token prediction, including pretraining at scale and assistant alignment concepts
- Working knowledge of the developer toolkit for building with LLMs, including prompting, embeddings for semantic search, RAG, and tools/function calling
- Hands-on experience choosing when to prompt, use RAG, or fine-tune, and evaluating outputs with basic guardrails for production use
Learning Roadmap
1.
Course Overview
Course Overview
Get familiar with large language models, their applications, and ethical considerations in AI.
2.
The Inference Journey
The Inference Journey
Learn about advanced LLM fundamentals, components, types, capabilities, examples, and limitations.
3.
The Training Journey
The Training Journey
3 Lessons
3 Lessons
Understand how the model is trained for next-token prediction and the four key steps it takes to get better at the process.
4.
Building with LLMs: The Developer’s Toolkit
Building with LLMs: The Developer’s Toolkit
6 Lessons
6 Lessons
Master effective prompt engineering, embeddings, and RAG for advanced LLM applications.
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Developed by MAANG Engineers
ABOUT THIS COURSE
In this course, you will learn how large language models work, what they are capable of, and where they are best applied. You will start with an introduction to LLM fundamentals, covering core components, basic architecture, model types, capabilities, limitations, and ethical considerations.
You will then explore the inference and training journeys of LLMs. This includes how text is processed through tokenization, embeddings, positional encodings, and attention to produce outputs, as well as how models are trained for next-token prediction at scale.
Finally, you will learn how to build with LLMs using a developer-focused toolkit. Topics include prompting, embeddings for semantic search, retrieval-augmented generation (RAG), tool and function calling, evaluation, and production considerations. By the end of this course, you will understand how LLMs actually work and apply them effectively in language-focused applications.
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Ramesh Perla
Senior Software Engineer @ Microsoft
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Jorge Astorga
AI Technical Program Manager @ Cruise
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Anthony Walker
@_webarchitect_
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