RAG-Driven Generative AI
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RAG-Driven Generative AI
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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
Skills you'll gain
Tools you'll learn
Details to know
March 2026
10 assignments
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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
1 video•Total 1 minute
- Dynamic RAG with Chroma and Hugging Face Llama - Overview Video•1 minute
5 readings•Total 100 minutes
- Introduction•10 minutes
- Installing the Environment•10 minutes
- Activating Session Time•30 minutes
- Querying the Collection•20 minutes
- Prompt and Retrieval•30 minutes
1 assignment•Total 10 minutes
- Dynamic RAG and AI System Integration•10 minutes
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
1 video•Total 1 minute
- Empowering AI Models Fine-Tuning RAG Data and Human Feedback - Overview Video•1 minute
3 readings•Total 60 minutes
- Introduction•40 minutes
- Using the fine-tuned OpenAI model•10 minutes
- Metrics•10 minutes
1 assignment•Total 10 minutes
- Enhancing AI Performance Through Data and Feedback•10 minutes
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.
What's included
1 video7 readings1 assignment
1 video•Total 1 minute
- RAG for Video Stock Production with Pinecone and OpenAI - Overview Video•1 minute
7 readings•Total 130 minutes
- Introduction•10 minutes
- The Environment of the Video Production Ecosystem•10 minutes
- Pipeline Generator and Commentator•10 minutes
- Video Download and Display Functions•30 minutes
- The Generator and the Commentator•10 minutes
- Pipeline Controller•30 minutes
- The Video Expert•30 minutes
1 assignment•Total 10 minutes
- RAG and AI in Video Production Fundamentals•10 minutes
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