AI & LLM Engineering Mastery: GenAI, RAG Complete Guide from Udemy

Become an AI Engineer and master Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI agents, and vector databases in this comprehensive hands-on course.

Whether a beginner or an experienced developer, this course will take you from zero to hero in building real-world AI-powered applications.

This course combines deep theoretical insights with hands-on projects, ensuring you understand AI model architectures, development and optimization strategies, and practical applications.

What You’ll Learn:

Deep Learning & Machine Learning Foundations
- Understand neural networks, activation functions, transformers, and the evolution of AI.
- Learn how modern AI models are trained, optimized, and deployed in real-world applications.
Master Large Language Models (LLMs) & Transformer-Based AI
- Deep dive into OpenAI models, and open-source AI frameworks.
- Build and deploy custom LLM-powered applications from scratch.
Retrieval-Augmented Generation (RAG) & AI-Powered Search
- Learn how AI retrieves knowledge using vector embeddings, FAISS, and ChromaDB.
- Implement scalable RAG systems for AI-powered document search and retrieval.
LangChain & AI Agent Workflows
- Build AI agents that autonomously retrieve, process, and generate information.
Fine-Tuning LLMs & Open-Source AI Models
- Fine-tune OpenAI, and LoRA models for custom applications.
- Learn how to optimize LLMs for better accuracy, efficiency, and scalability.
Vector Databases & AI-Driven Knowledge Retrieval
- Work with FAISS, ChromaDB, and vector-based AI search workflows.
- Develop AI systems that retrieve and process structured & unstructured data.
Hands-on with AI Deployment & Real-World Applications
- Build AI-powered chatbots, multimodal RAG applications, and AI automation tools.

Who Should Take This Course?

Aspiring AI Engineers & Data Scientists – Looking to master LLMs, AI retrieval, and search systems.
Developers & Software Engineers – Who want to integrate AI into their applications.
Machine Learning Enthusiasts – Seeking a deep dive into AI, GenAI, and AI-powered search.
Tech Entrepreneurs & Product Managers – Wanting to build AI-driven SaaS products.
Students & AI Beginners – Who need a structured, step-by-step path from beginner to expert.

Course Requirements

No prior AI experience required – the course takes you from beginner to expert.
Basic Python knowledge (recommended but not required - Python Fundamentals Included in the course).
Familiarity with APIs & JSON is helpful but not mandatory.
A computer with internet access for hands-on development.

Why Take This Course?

Comprehensive AI Training: Covers LLMs
Hands-On Projects: Every concept is reinforced with real-world AI applications.
Up-to-Date & Practical: Learn cutting-edge AI techniques & tools used in top tech companies.
Zero to Hero Approach: Designed for absolute beginners & experienced developers alike.

Master AI Engineering and become an expert in GenAI, LLMs, and RAG today.

What's inside

Learning objectives

Master the architecture and workflow of a rag system for processing pdfs and multimodal data.
Master the fundamentals of ai, machine learning and deep learning (basics)
Master langchain tools, frameworks, and workflows, including embedding techniques and retrievers.

Fine-tuning models with openai, lora, and other techniques to customize ai responses.
Develop ai-driven applications with advanced rag techniques, multimodal search, and ai agents for real-world use cases.

Master the architecture and workflow of a rag system for processing pdfs and multimodal data.
Master the fundamentals of ai, machine learning and deep learning (basics)
Master langchain tools, frameworks, and workflows, including embedding techniques and retrievers.
Fine-tuning models with openai, lora, and other techniques to customize ai responses.
Develop ai-driven applications with advanced rag techniques, multimodal search, and ai agents for real-world use cases.

Syllabus

Introduction

DEMO - What You'll Build in this Course

Course Structure

How To Get The Most from This Course

Career center

Learners who complete AI & LLM Engineering Mastery: GenAI, RAG Complete Guide will develop knowledge and skills that may be useful to these careers:

AI Engineer

An AI Engineer builds, develops, and deploys intelligent systems, focusing on practical applications of artificial intelligence. This professional designs and implements machine learning models, integrates AI capabilities into existing platforms, and optimizes performance for real-world scenarios. This comprehensive course, "AI & LLM Engineering Mastery," directly prepares you for this role by covering the full spectrum of modern AI development. You will master Large Language Models, Generative AI, Retrieval-Augmented Generation, and AI agents, essential competencies for any aspiring AI Engineer. The hands-on projects, from building chatbots to multimodal RAG applications, provide invaluable practical experience in deploying AI solutions.

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Generative AI Developer

A Generative AI Developer specializes in creating and implementing AI systems capable of generating new content, such as text, images, or code. This role involves understanding generative models, adapting them for specific tasks, and integrating them into various applications. This course is exceptionally well-suited for a Generative AI Developer, as it focuses entirely on Generative AI and Large Language Models. You will gain mastery in building and deploying custom LLM-powered applications from scratch, utilizing advanced techniques like Retrieval-Augmented Generation. The practical skills in fine-tuning open-source AI models and developing multimodal RAG applications are directly aligned with the core responsibilities of this cutting-edge field.

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Machine Learning Engineer

As a Machine Learning Engineer, you are responsible for designing, building, and deploying machine learning models and systems. This often involves data preprocessing, model selection, training, evaluation, and ensuring models operate efficiently in production environments. This course helps you build a strong foundation for this career by offering deep dives into machine learning and deep learning foundations, including neural networks and transformers. Furthermore, its extensive focus on developing and optimizing Large Language Models, along with practical deployment strategies, provides critical skills for a Machine Learning Engineer. The ability to fine-tune LLMs and build AI-powered applications is directly applicable to advanced ML engineering.

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Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that allow computers to understand, interpret, and generate human language. This involves working with text analysis, speech recognition, and language generation tasks, often utilizing advanced machine learning techniques. For an aspiring Natural Language Processing Engineer, this course offers profound insights into the foundational elements of modern NLP. Specifically, mastering Large Language Models, which are at the forefront of NLP, and deploying custom LLM-powered applications are core competencies. The course's emphasis on Retrieval-Augmented Generation systems and fine-tuning models for specific linguistic tasks provides highly relevant and sought-after skills in this dynamic field.

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AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of complex AI systems, ensuring they meet business requirements and are scalable, robust, and secure. This role requires a deep understanding of AI technologies, system integration, and architectural best practices. This course provides comprehensive knowledge essential for an AI Solutions Architect. By mastering LLM architectures, Retrieval-Augmented Generation, vector databases, and AI agent workflows, you gain the technical expertise to design intelligent solutions. The focus on deployment and real-world applications helps in understanding the practical considerations and challenges of building scalable AI-powered systems.

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Deep Learning Engineer

A Deep Learning Engineer designs, implements, and optimizes neural network architectures to solve complex problems across various domains. This often involves working with large datasets, advanced algorithms, and specialized hardware to train and deploy sophisticated models. This course can help build a strong foundation for a career as a Deep Learning Engineer. It provides deep learning and machine learning foundations, including understanding neural networks and transformers. Crucially, the practical mastery of Large Language Models, which are prominent deep learning models, along with optimization strategies and fine-tuning techniques, directly equips you with the skills needed to build and deploy advanced deep learning solutions in real-world applications.

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Applied Scientist AI

An Applied Scientist AI bridges the gap between fundamental research and practical application, developing innovative AI solutions for specific problems. This role often involves experimenting with new algorithms, prototyping models, and collaborating with engineering teams to bring ideas to production. This course provides the blend of "deep theoretical insights" and "hands-on projects" that is highly valuable for an Applied Scientist AI. Mastering LLMs, GenAI, and RAG systems allows for the development and testing of advanced AI applications. While pure research often requires an advanced degree, the practical skills in model optimization and fine-tuning are crucial for transforming novel concepts into tangible solutions.

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Vector Database Specialist

A Vector Database Specialist focuses on designing, implementing, and managing systems that store, index, and query high-dimensional vector embeddings, which are crucial for semantic search, recommendation engines, and modern AI applications. For this emerging role, this course provides highly specific and practical expertise. You will learn to work extensively with vector databases like FAISS and ChromaDB, and master AI-driven knowledge retrieval workflows. This deep dive into vector embeddings and their application in scalable Retrieval-Augmented Generation systems directly equips you with the foundational understanding and hands-on skills required to excel as a Vector Database Specialist.

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Research Engineer AI

A Research Engineer AI combines scientific research principles with engineering practices to develop and implement cutting-edge AI technologies. This role involves prototyping new algorithms, conducting experiments, and bridging the gap between theoretical advancements and practical applications. For a Research Engineer AI, this course offers a strong foundation in "deep theoretical insights" combined with extensive hands-on experience in building and optimizing AI systems. Mastery of LLM architectures, fine-tuning techniques, and the development of AI agents provides concrete skills for implementing and testing novel research ideas. While often an advanced degree is typical for this profession, the practical application focus of this course is a distinct asset.

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Data Scientist Machine Learning Specialist

A Data Scientist Machine Learning Specialist focuses on extracting insights from data and building predictive or analytical models, often with a strong emphasis on machine learning techniques. They analyze complex datasets, develop algorithms, and apply statistical modeling to solve business problems. This course is highly relevant for a Data Scientist Machine Learning Specialist looking to specialize in advanced AI. While it establishes machine learning foundations, its core strength lies in mastering Large Language Models, Generative AI, and RAG. This specialization allows you to develop sophisticated AI-powered applications for data analysis, information retrieval, and intelligent decision-making, offering a distinct advantage in this evolving field.

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Prompt Engineer

A Prompt Engineer specializes in crafting effective queries and instructions for large language models to elicit desired responses and behaviors. This critical role ensures AI systems deliver accurate, relevant, and creative outputs. For an aspiring Prompt Engineer, this course provides a deep understanding of the underlying mechanics of LLMs, Generative AI, and Retrieval-Augmented Generation systems. By mastering AI model architectures, fine-tuning techniques, and AI agent workflows, you gain profound insight into how these models process information and generate responses. This knowledge is invaluable for designing sophisticated prompts and understanding the nuances of AI model interaction beyond surface-level usage.

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Full Stack Developer AI Specialist

A Full Stack Developer AI Specialist possesses expertise across both front-end and back-end development, with an additional focus on integrating and developing artificial intelligence capabilities. This role involves building complete applications that harness the power of AI to enhance user experience and functionality. This course offers comprehensive preparation for a Full Stack Developer AI Specialist by providing a strong Python foundation and extensive hands-on experience in building and deploying real-world AI-powered applications. Mastering LLMs, RAG, and AI agents enables the integration of intelligent features, while understanding AI deployment ensures these applications are robust and scalable.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer focuses on the deployment, monitoring, and maintenance of machine learning models in production environments. This ensures the continuous, efficient, and reliable operation of AI systems at scale. This course provides fundamental knowledge for a Machine Learning Operations Engineer. While its primary focus is on building and deploying, the mastery of AI model architectures, understanding development and optimization strategies, and hands-on experience with AI deployment are foundational. This knowledge provides critical context for managing the lifecycle of Large Language Models and Retrieval-Augmented Generation systems, preparing one to oversee their operational aspects effectively.

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AI Product Manager

An AI Product Manager guides the development and strategy of AI-driven products, bridging the gap between technical teams, business needs, and user requirements. This role involves defining product vision, features, and roadmaps for intelligent solutions. This course may be useful for an AI Product Manager. While it is highly technical, mastering LLMs, Generative AI, and RAG systems provides profound insight into the capabilities and limitations of AI technologies. Understanding AI deployment and real-world applications helps in assessing technical feasibility, defining product specifications, and communicating effectively with engineering teams, ensuring the successful development of AI-driven SaaS products.

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AI Researcher

An AI Researcher explores and develops new artificial intelligence theories, algorithms, and techniques, often pushing the boundaries of current knowledge. This role typically involves conducting experiments, publishing findings, and contributing to the academic or industrial AI community. This course may be helpful for an AI Researcher by providing "deep theoretical insights" into machine learning and deep learning foundations, along with practical mastery of LLMs and Generative AI. While often an advanced degree is typical for pure research, the hands-on experience in fine-tuning models and developing advanced RAG techniques offers valuable implementation skills for testing hypotheses and building prototypes in applied AI research contexts.

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