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Master Retrieval Augmented Generation & Data Pipelines

Name: Master Retrieval Augmented Generation & Data Pipelines
Rating: 4.3 (42 reviews)

Build retrieval augmented generation, LLMs, and scalable data pipelines with hands-on projects

Role Play

Created byStarweaver Experts, Ritesh Vajariya, Paul Siegel

Last updated 5/2026

English

What you'll learn

Build data pipelines for AI-ready systems, covering data pipeline basics, validation, and enterprise-grade data processing workflows.
Understand retrieval augmented generation (RAG), including what is RAG and how RAG pipelines work with embeddings and vector search.
Implement advanced RAG architecture with context management, metadata filtering, and optimization for real-world AI use cases.
Develop customer support solutions using RAG retrieval augmented generation with context-aware personalization and tracking.
Gain hands-on skills in how to implement RAG, including RAG agents, vector databases, and enterprise AI pipeline design.

Course content

10 sections • 46 lectures • 2h 51m total length

Course Introduction4:11
Overview of the course for students to understand what is the course about.

Generative AI Impact on Engineering4:58
Comprehensive overview of how GenAI is transforming software engineering and development practices.
Fundamentals of Generative AI Systems Architecture3:08
Detailed breakdown of fundamental components that power modern generative AI systems.
Setting Up GenAI Development Environments: Local & Cloud5:34
Step-by-step walkthrough of setting up local and cloud development environments for GenAI.
Enterprise Implementation Success Stories3:10
Analysis of successful GenAI implementations across different industry sectors with metrics.
Hands-On-Learning: Introduction to Generative AI0:14
A Survey of Generative Artificial Intelligence0:15
Introduction to Generative AI

LLM Components and Core Mechanics5:22
Deep dive into the foundational elements that make up modern language models.
Enterprise LLM Model Comparison3:08
Comprehensive comparison of leading LLM models with their strengths and use cases.
LLM Integration and API Setup5:59
Practical demonstration of integrating different LLM APIs into development workflows.
Strategic Model Selection Framework3:13
Systematic approach to choosing the right LLM based on project requirements and constraints.
Hands-On-Learning: LLM Integration and API Setup0:17
A Brief Survey of Large Language Models0:14
Large Language Models

Enterprise GenAI Application Matrix5:29
Structured analysis of GenAI applications across different business functions and departments.
Industry-Specific Solution Architecture4:01
Detailed examination of GenAI solutions customized for different industry requirements.
Support Assistant System Design7:51
Complete walkthrough of designing and planning a customer support AI assistant system.
ROI Measurement and Metrics3:57
Comprehensive framework for measuring and tracking GenAI implementation success metrics.
Hands-On-Learning: Support Assistant System Design0:16
Generative AI Use Cases: A Primer0:14
GenAI Use Cases
Persuading Leadership to Approve a Phased Rollout Instead of a Full RAG Launch

Data Pipeline Requirements Analysis4:31
Comprehensive overview of data requirements and preprocessing needs for GenAI systems.
Enterprise Data Pipeline Design4:11
Detailed examination of data pipeline architectures and integration points for RAG.
Data Processing System Implementation8:36
Hands-on demonstration of building robust data processing pipelines for GenAI.
Data Quality Validation Framework3:57
Complete framework for ensuring and maintaining high-quality data for RAG systems.
Hands-On-Learnings: Data Processing0:17
Data Preparation for Machine Learning0:14
Data Processing

RAG System Architecture Design5:20
In-depth exploration of RAG architecture components and their interactions.
RAG Architecture: Component Integration Fundamentals4:09
Comprehensive breakdown of essential RAG components and their integration patterns.
RAG Implementation Best Practices9:04
Step-by-step guide to implementing a basic RAG system with best practices.
RAG System Testing Protocol4:16
Systematic approach to testing and validating RAG system performance and accuracy.
Hands-On-Learning: RAG Fundamentals0:17
Retrieval-Augmented Generation: Recent Advances and Future Directions0:14
RAG Fundamentals

Advanced RAG Pattern Analysis6:53
Detailed examination of complex RAG patterns and their enterprise applications.
Performance Optimization Techniques Framework5:04
Comprehensive strategies for optimizing RAG system performance and response time.
Complex RAG System Development13:14
Complete implementation of advanced RAG features and optimization techniques.
Enterprise Integration Best Practices5:00
Best practices for integrating RAG systems with existing enterprise infrastructure.
Hands-On-Learning: Advanced RAG: Complex RAG System Development0:18
Retrieval-Augmented Generation: Recent Advances and Future Directions0:14
Advanced RAG

Support Documentation Processing Framework5:32
Systematic approach to processing and structuring support documentation for RAG.
Knowledge Base Architecture Design5:15
Detailed design principles for building effective support knowledge base systems.
Support RAG Implementation Guide10:56
Complete walkthrough of building a RAG system for customer support applications.
Response Quality Enhancement Strategy5:51
Advanced techniques for improving RAG response accuracy in support scenarios.
Hands-On-Learning: RAG for Customer Support: Production-Ready System0:18
Building an Intelligent Customer Support Chatbot with RAG: A Complete Guide0:20
RAG for Customer Support

Requirements

To get the most out of this course, learners should have a strong foundation in Python programming and familiarity with databases and data processing workflows.
A solid grasp of machine learning principles is essential, as is experience with APIs and web services. Exposure to cloud-based infrastructure and tools will also be highly beneficial. This will support hands-on implementation of Retrieval-Augmented Generation (RAG) systems and enterprise data pipelines.

Description

Ready to make AI systems work with your organization’s unique knowledge and data? Most AI implementations fail because they cannot effectively access and process enterprise information. This course helps you overcome that challenge by mastering data pipelines, gen AI and retrieval-augmented generation (RAG) systems that connect AI models with real-world data.

You will learn what retrieval augmented generation (RAG) is and how retrieval augmented generation works, while building systems that transform raw enterprise data into intelligent, context-aware responses. This course turns you into an AI engineer capable of designing scalable RAG pipelines and advanced AI automation workflows.

You’ll master data pipeline engineering, including data warehouse pipeline design, document processing, and transforming unstructured data into AI-ready formats. You will also explore data pipeline vs warehouse concepts and understand the meaning of data pipeline in enterprise AI systems.

This comprehensive program provides a practical approach to retrieval augmented generation systems, covering RAG architecture, embeddings, vector databases, and intelligent retrieval strategies. You’ll also learn what a RAG pipeline is, what RAG is in GenAI, and how to implement RAG AI systems for real-world applications.

Through hands-on labs, you will build production-ready retrieval augmented generation software with adaptive orchestration, personalization, and monitoring. You’ll explore agentic AI workflows and understand what RAG agents are, enabling intelligent and scalable knowledge systems.

You will also gain expertise in:

Designing enterprise-grade data pipelines for AI-ready processing

Implementing retrieval-augmented generation with vector search and embeddings

Optimizing RAG pipelines with reranking, metadata filtering, and adaptive strategies

Integrating large language models (LLMs) into AI engineering workflows

Applying AI automation and prompt engineering for high-quality outputs

By the end of this course, you will confidently design and deploy end-to-end RAG systems that transform how organizations access and use knowledge. You will build scalable systems capable of handling millions of documents and delivering precise, context-aware responses.

Learning Approach

This course follows a learn-by-doing model:

Conceptual lectures covering RAG fundamentals and best practices

Hands-on labs for building data pipelines and RAG architectures

Quizzes to reinforce concepts and assess understanding

Capstone project to implement a full retrieval augmented generation pipeline

Main Outcome

Learners will be able to architect and deploy end-to-end retrieval-augmented generation (RAG) systems integrated with advanced data pipelines, vector databases, and intelligent retrieval strategies.

Learning Objectives

Build enterprise-grade data pipelines with validation and AI-ready transformation

Implement advanced RAG architecture and vector search systems

Optimize retrieval augmented generation pipelines for performance and scalability

Develop real-world RAG AI applications for customer support and knowledge systems

Apply prompt engineering for LLM optimization

Key Takeaways

Enterprise data pipeline engineering for generative AI

Production-ready retrieval-augmented generation systems

Vector database design and semantic search

Intelligent knowledge management using RAG AI

Advanced AI engineering and prompt optimization

Skills Gained

AI Data Pipeline Engineering

Advanced RAG System Development

Vector Database Architecture

Intelligent Knowledge Systems

Prompt Engineering for RAG LLM Applications

Enrol Now

Take the next step in your AI engineering journey. Master data pipelines and retrieval-augmented generation (RAG) - the most in-demand skills in modern artificial intelligence.

Build intelligent systems, advance your career, and become the expert organizations need to unlock the full potential of their data.

Who this course is for:

This course is designed for technical professionals working at the intersection of data pipelines and retrieval-augmented generation (RAG) within modern AI systems. It is ideal for data engineers transitioning into AI engineering workflows, ML engineers focused on building robust data pipelines, and software engineers developing intelligent systems powered by artificial intelligence.
The program is particularly relevant for AI engineers and AI/ML specialists implementing retrieval augmented generation architectures, including RAG pipelines, RAG architecture, and production-ready retrieval-augmented generation systems. Learners will gain clarity on what is retrieval augmented generation (RAG), how retrieval augmented generation works, and why RAG is important in modern AI automation and knowledge-driven applications.
The curriculum speaks directly to professionals building or maintaining production-grade systems, where data integrity, contextual relevance, and system performance is critical. It also addresses practical challenges and explains how data pipelines work in real-world AI environments.
By combining data pipelines with retrieval augmented generation (RAG), this course equips learners to design scalable, high-performance systems that leverage context-aware intelligence. It is especially valuable for those implementing RAG agents, exploring how to implement RAG, or applying a practical approach to retrieval augmented generation systems in production settings.

Master Retrieval Augmented Generation & Data Pipelines

What you'll learn

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Course content

Course Introduction1 lecture • 4min

Introduction to Generative AI6 lectures • 17min

Large Language Models6 lectures • 18min

GenAI Use Cases7 lectures • 22min

Data Processing6 lectures • 22min

RAG Fundamentals6 lectures • 23min

Advanced RAG6 lectures • 31min

RAG for Customer Support6 lectures • 28min

Course Conclusion2 lectures • 4min

Bonus Lecture1 lecture • 1min

Requirements

Description

Who this course is for: