- Course
- AI
Practical Application of LLMs
Learn how to harness the power of large language models in real-world settings. Explore model architectures, performance tuning, API vs. local deployment, fine-tuning, RAG, and AI agents—plus demos using tools like LangChain, CrewAI, and n8n.
What you'll learn
Large language models (LLMs) are reshaping how businesses handle content creation, customer interactions, data analysis, and decision-making. However, to truly capitalize on their capabilities, organizations need a practical understanding of how LLMs are built, deployed, and fine-tuned for specific outcomes.
In this course, Practical Application of LLMs, you’ll learn about the inner workings and capabilities of these models, such as decoder-only LLMs.
First, you'll focus on applying LLMs to real-world problems.
Next, you'll understand how to fine-tune models for domain-specific needs and build retrieval-augmented generation (RAG) systems.
Finally, you’ll explore AI agents—how to design them, the types that exist, and how they operate in production environments.
By the end of this course, you’ll be equipped with the technical and strategic knowledge to deploy LLMs and AI agents effectively—turning raw model power into tailored business solutions.
Table of contents
About the author
Tom Taulli is a developer and writer. He has been programming since he was in high school, when he wrote computer programs for magazines (yes, in the 1980s, there were publications that had code listings!). When he got into college, he started a company that sold Windows software for exam preparation. He would then go on to found other startups. Along the way, Tom has been a writer of various books like Artificial Intelligence Basics and the RPA Handbook. You can reach him taulli.com.