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.
4.7
19 lessons
2h
Created by Khayyam Hashmi
What's included
- 19 lessons across 5 modules
- AI Tutor on every lesson
Why this course
- Design Effective PromptsCreate prompts that guide large language models to produce accurate and contextually relevant outputs in real-world applications.
- Implement Retrieval-Augmented GenerationUtilize RAG techniques to enhance LLM capabilities by integrating external knowledge bases for improved answer accuracy.
- Evaluate LLM PerformanceApply evaluation methods and safety guardrails to ensure reliable and secure deployment of LLM-powered applications.
- Architect AI ApplicationsBuild and deploy AI applications using large language models, leveraging their capabilities for conversational agents and automation.
Who wrote it
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Dr. Khayyam Hashmi is a seasoned computer scientist and Generative AI and Machine Learning specialist. As a researcher, educator, and the VP of Technical Content at Educative, he is dedicated to making complex computing concepts accessible for learners worldwide. With a PhD in Computer Science from Wayne State University and academic publications in service-oriented systems and intelligent computing, Khayyam excels at fusing an academic perspective with practical software engineering insights. Khayyam’s courses, writing, and research spans technical topics including adaptive AI systems, neural networks, Intelligent tutoring systems, and production-ready software design.
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What You'll Learn
- Explain the fundamentals of large language models, including their architecture, training dynamics, and core capabilities.
- Describe the processes of tokenization and embeddings, and their roles in transforming text for language models.
- Analyze the attention mechanism and its importance in generating context-aware representations in large language models.
- Implement effective prompting techniques and retrieval-augmented generation to enhance LLM performance in practical applications.
- Evaluate the training loop and pretraining processes that enable large language models to learn from vast datasets.
- Assess the deployment strategies and safety measures necessary for transitioning LLM-powered applications to production.
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.
Certificate of Completion
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About this course
WHY I WROTE THIS COURSE
Large language models (LLMs) are at the core of today’s AI transformation, powering everything from conversational agents to code generation and enterprise automation. As adoption accelerates, understanding how LLMs actually work, and how to use them effectively in real systems, is no longer optional for developers and data professionals.
I built this course from my work in neural networks and intelligent systems, where LLMs represent a shift from traditional modeling to probabilistic reasoning at scale. A recurring pattern I observed was that many practitioners could use APIs but lacked a clear mental model of how LLMs process language, make decisions, and fail in edge cases. This course is designed to bridge that gap with a systems-level perspective.
You’ll learn LLM fundamentals from first principles, covering architecture, tokenization, embeddings, attention, and training dynamics, before moving into practical workflows like prompting, retrieval-augmented generation (RAG), and tool integration. Each concept is tied to how LLMs are actually deployed in production systems.
Engineers and researchers are already building on these foundations to create real-world AI applications. If you want to go beyond surface-level usage of LLMs, this is where you begin.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Trusted by 3.1 million developers working at companies
R
Ramesh Perla
Senior Software Engineer @ Microsoft
J
Jorge Astorga
AI Technical Program Manager @ Cruise
A
Anthony Walker
@_webarchitect_
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