RAG-Driven Generative AI

RAG-Driven Generative AI

Instructor: Packt - Course Instructors

Access provided by Assam down town University

10 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Scale RAG pipelines to handle large datasets efficiently
Implement techniques that reduce hallucinations and improve response accuracy
Customize and scale RAG-driven AI systems across different domains

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 10 modules in this course

This course introduces the powerful concept of Retrieval-Augmented Generation (RAG), a technique used to optimize the performance, accuracy, and cost of generative AI systems. Focused on building AI pipelines with LlamaIndex, Deep Lake, and Pinecone, this course will equip you with the skills to create robust AI models capable of handling complex datasets and delivering traceable, context-aware outputs.

You will explore how to scale RAG pipelines, implement strategies to minimize hallucinations, and improve response accuracy across multimodal AI systems. By the end of the course, you will have hands-on experience optimizing these systems for real-world applications, empowering you to enhance decision-making and operational efficiency. What sets this course apart is its unique combination of theory and practical implementation. By working with cutting-edge tools like LlamaIndex and Pinecone, you'll understand how to balance cost, performance, and accuracy, while gaining insight into the broader context of AI pipelines and decision-making. This course is ideal for data scientists, AI engineers, and MLOps professionals who are looking to expand their expertise in RAG and generative AI. A basic understanding of machine learning concepts is recommended, as the course builds on these foundations to explore more advanced techniques.

In this section, we explore Retrieval Augmented Generation (RAG) frameworks, focusing on naive, advanced, and modular configurations. We implement Python-based RAG systems for improved AI accuracy and adaptability.

What's included

2 videos6 readings1 assignment

2 videos Total 2 minutes

Course Overview 1 minute
Why Retrieval Augmented Generation? - Overview Video 1 minute

6 readings Total 65 minutes

Introduction 15 minutes
Human Feedback (E2) 10 minutes
Advanced techniques and evaluation 10 minutes
Metrics 10 minutes
Generation 10 minutes
Generation 10 minutes

1 assignment Total 10 minutes

Exploring Retrieval Augmented Generation 10 minutes

In this section, we cover building and managing RAG pipelines with Deep Lake and OpenAI for efficient AI data handling.

What's included

1 video4 readings1 assignment

1 video Total 1 minute

RAG Embedding Vector Stores with Deep Lake and OpenAI - Overview Video 1 minute

4 readings Total 70 minutes

Introduction 20 minutes
Authentication Process 20 minutes
Retrieving a Batch of Prepared Documents 10 minutes
3. Augmented input generation 20 minutes

1 assignment Total 10 minutes

RAG Embedding Vector Stores with Deep Lake and OpenAI 10 minutes

In this section, we explore index-based RAG pipelines using LlamaIndex, Deep Lake, and OpenAI to enhance traceability, precision, and control in AI-driven data retrieval and generation.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI - Overview Video 1 minute

5 readings Total 90 minutes

Introduction 10 minutes
Building a Semantic Search Engine and Generative Agent for Drone Technology 20 minutes
Creating and Populating a Deep Lake Vector Store 10 minutes
Vector Store Index Query Engine 20 minutes
Tree Index Query Engine 30 minutes

1 assignment Total 10 minutes

Indexing and Retrieval in AI Systems 10 minutes

In this section, we explore multimodal modular RAG for drone technology, integrating text and image data retrieval, generation, and performance evaluation using LLMs and MMLLMs.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Multimodal Modular RAG for Drone Technology - Overview Video 1 minute

5 readings Total 70 minutes

Introduction 10 minutes
Building a Multimodal Modular RAG Program for Drone Technology 10 minutes
Loading and Visualizing the Multimodal Dataset 20 minutes
Building a Multimodal Query Engine 10 minutes
Multimodal Modular Summary 20 minutes

1 assignment Total 10 minutes

Multimodal RAG in Drone Applications 10 minutes

In this section, we explore adaptive RAG with human feedback loops, focusing on improving retrieval quality and integrating expert input.

What's included

1 video4 readings1 assignment

1 video Total 1 minute

Boosting RAG Performance with Expert Human Feedback - Overview Video 1 minute

4 readings Total 65 minutes

Introduction 10 minutes
Building Hybrid Adaptive RAG in Python 20 minutes
No RAG 5 minutes
RAG with No Human-Expert Feedback Documents 30 minutes

1 assignment Total 10 minutes

Enhancing RAG Systems with Human and Data-Driven Insights 10 minutes

In this section, we explore scalable RAG techniques for bank customer data using Pinecone and OpenAI. Key concepts include EDA, vector scaling, and AI-driven recommendations to reduce churn.

What's included

1 video7 readings1 assignment

1 video Total 1 minute

Scaling RAG Bank Customer Data with Pinecone - Overview Video 1 minute

7 readings Total 120 minutes

Introduction 10 minutes
Collecting the Dataset 10 minutes
Data Preparation and Clustering 10 minutes
Scaling a Pinecone Index (Vector Store) 10 minutes
Chunking 30 minutes
Upserting 20 minutes
RAG Generative AI 30 minutes

1 assignment Total 10 minutes

Scaling RAG Bank Customer Data with Pinecone 10 minutes

In this section, we explore building scalable RAG systems using knowledge graphs, implementing the Wikipedia API, populating a Deep Lake vector store, and constructing a LlamaIndex knowledge graph for semantic search.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex - Overview Video 1 minute

5 readings Total 80 minutes

Introduction 10 minutes
Building Graphs from Trees 20 minutes
Preparing the Data for Upsertion 30 minutes
Re-ranking 10 minutes
Metric Calculation and Display 10 minutes

1 assignment Total 10 minutes

Exploring Knowledge-Graph-Based RAG Systems 10 minutes

In this section, we explore dynamic RAG using Chroma and Llama, focusing on embedding and querying temporary data for real-time decision-making with open-source tools.

What's included

1 video5 readings1 assignment

In this section, we explore RAG data reduction through fine-tuning, focusing on preparing JSONL datasets and evaluating model performance with OpenAI metrics for improved accuracy and cost-effectiveness.

What's included

1 video3 readings1 assignment

In this section, we explore RAG pipeline implementation for video generation, embedding video comments in Pinecone, and enhancing labels with GPT-4o analysis for efficient video stock production.