AI & Generative AI
Understanding Large Language Models
This course was developed from a series of live webinars delivered by CBXperts Academy, with each module directly corresponding to a webinar session. Every concept has been carefully explained for a mixed audience: if you are a developer, you will find the technical depth you need; if you are a business leader or product manager, you will find the conceptual clarity that helps you make informed decisions about AI. The course is grounded in the book Building LLMs for Production by Louis-François Bouchard and Louie Peters (Towards AI, 2024) , one of the most highly recommended practitioner resources in the LLM field, endorsed by engineers from Meta, AWS, Google, OpenAI, and Anthropic. This document covers the first major arc: from the foundations of language models all the way through to production-grade Retrieval-Augmented Generation (RAG).
1 students10m25 lessonsBEGINNER
Instructor: Anchal Gupta
₹5
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- Full lifetime access
- Certificate of completion
- 25 lessons
What you'll learn
- The history, terminology, and fundamental concepts that define Large Language Models
- How the Transformer architecture works — the engine powering virtually every major AI system today
- The real-world challenges of deploying LLMs: hallucinations, bias, evaluation, and output control
- How to write effective prompts using proven techniques from zero-shot to Chain-of-Thought
- What Retrieval-Augmented Generation is, how it works end-to-end, and when to use it
- How to load, split, embed, and retrieve documents in a production RAG pipeline
- How to use LangChain to build maintainable, composable LLM pipelines
- The full landscape of the LLM ecosystem: proprietary models, open-source models, and tooling frameworks
Skills You Will Gain
- Foundational AI Literacy
- Transformer Architecture
- Prompt Engineering
- Responsible AI Deployment
- Retrieval-Augmented Generation (RAG)
- LangChain Development
- LLM Ecosystem Navigation
Course Content
What Are Large Language Models?
Terminology, history, and the conceptual foundations every AI practitioner must understand
How LLMs Are Built: Architecture & Landscape
Inside the Transformer - and the modern AI model ecosystem
LLMs in the Real World: Challenges & Evaluation
Why LLMs fail, how to measure them, and what can be done about it
The Art and Science of Prompting
The complete practitioner's guide to communicating with LLMs effectively
Retrieval-Augmented Generation (RAG)
The dominant production pattern for connecting LLMs to real-world knowledge
Introduction to LangChain and LlamaIndex
Building production-grade, maintainable LLM pipelines