About The 8-Hour Primer Course

This course is an excellent first step for those looking to understand how Large Language Models (LLMs) work and how to start building on top of them. Throughout the 8 hours, we’ll introduce foundational concepts, tools, and frameworks that will give you the confidence to begin using LLMs in your own projects or work environment. Although this course is kept coding-light, we highlight the typical software languages (Python, TypeScript, etc.) and APIs most commonly used for advanced LLM development.

In short, we aim to teach you:

1. Core concepts of LLMs (no heavy math or theory) so that you appreciate both their capabilities and constraints/limitations.
2. How to use LLMs to assist with everyday tasks in various industries and domains.
3. How to build more complex LLM pipelines with no code “Agent builder” platforms.
4. Why and how you can develop custom (high code) LLM pipelines to achieve reliable, domain-specific solutions.
5. In an included bonus (optional) final part of the course (and an additional ~4 hours), we cover further advanced LLM techniques and more skills to prepare you for the future of LLMs.

This not only prepares you to use LLMs effectively today but also sets the stage for deeper learning—whether through our advanced “From Beginner to Advanced LLM Developer” course or by continuing with your own self-guided exploration.

About Towards AI

Towards AI has been teaching AI for over 5 years, and over 500,000 people have learned from our tutorials and articles. You may have also read our best-selling LLM book, *Building LLMs for Production (Amazon)*, or taken our three-part GenAI:360 course series—released last year in partnership with Intel and Activeloop—which reached over 40,000 students.

Recently, we launched our extremely in-depth “From Beginner to Advanced LLM Developer” Python-based course, featuring 90+ advanced lessons, practical projects, and 60+ hours of content that culminates in a final working project. However, this 8-hour course (+4 hours optional further lessons) is a lighter, no-code introduction—an LLM primer designed to get you up to speed on using and customizing LLMs. While there are hands-on exercises, we focus primarily on conceptual understanding, demonstrations, and minimal “nuts-and-bolts” coding. This makes the course suitable for newer software developers in any language and more experienced tech professionals who want a broader, language-agnostic foundation in AI and LLMs. We note LLM Developer tools and APIs often use Python but also have typescript and other options available.

Who Is This Course For?

• Developers (of any language) who want an easy-to-follow, conceptual overview of LLMs before diving into code-heavy projects.
• Machine Learning Engineers looking to augment their existing skill set with prompt engineering, RAG, Fine-Tuning, data curation for LLMs, and more.
• Technical Product Managers, Entrepreneurs, and Tech Enthusiasts needing to evaluate LLMs, understand costs and performance trade-offs and communicate effectively with stakeholders.

No prior AI or math knowledge is required—just curiosity and motivation to learn.

Course Prerequisites

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Basic Software or Tech Familiarity: While we won’t dive into code, it helps to understand general programming and software concepts.

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Thanks to modern tools and platforms, it has become increasingly accessible to use and even build basic applications with LLMs. While no-code platforms allow non-programmers to create simple applications, having basic programming knowledge significantly expands what you can build. For developers getting started with generative AI, a working knowledge of Python (or another programming language) is typically sufficient - deep expertise in AI, neural networks, or LLM architecture isn't required for many applications. This accessibility is enabled by two key factors: First, pre-trained foundation models provide sophisticated capabilities out-of-the-box, reducing the complexity of implementation compared to traditional software development. Second, AI-powered development tools like GitHub Copilot and Cursor, along with interactive AI assistants like ChatGPT, provide valuable support for code generation, code debugging, and learning.