Example Curriculum
Section 1: Introduction to Retrieval Augmented Generation (RAG) Systems
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Section 2: Basics of Prompt Engineering, Python and OpenAI API
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Section 3: Prompt Engineering Basics
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Section 4: Understanding LLMs Part 1
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Section 5: Python for RAG and GenAI
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- Game Plan for Python for RAG and GenAI (1:34)
- Loops (5:18)
- Loops: Easy Level (8:31)
- Loops: Medium Level - Part 1 (3:45)
- Loops: Medium Level - Part 2 (3:55)
- Loops: Hard Level (2:56)
- Functions (4:43)
- Functions: Easy Level - Part 1 (4:06)
- Functions: Easy Level - Part 2 (1:30)
- Functions: Medium Level - Part 1 (2:49)
- Functions: Medium Level - Part 2 (3:07)
- Functions: Hard Level (7:01)
- Introduction to Classes (4:51)
- Classes: Easy Level - Part 1 (10:29)
- Classes: Easy Level - Part 2 (3:52)
- Classes: Medium Level (8:42)
Section 6: Understanding LLMs Part 2
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Section 7: OpenAI API
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- Overview: Working with the OpenAI API (3:47)
- OpenAI API for Text (4:52)
- Setting Up OpenAI API Key (5:08)
- OpenAI API (5:02)
- Generating Text with OpenAI API (6:37)
- OpenAI API Parameters (6:55)
- OpenAI API for Images (4:51)
- With Image URL (9:19)
- With Image in Base64 (10:08)
- Adding Few-Shot Prompting (6:26)
- What Did You Learn in this Section? (3:50)
Section 8: Understanding LLMs Part 3
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Section 9: CAPSTONE PROJECT: Deploy With Lovable
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Section 10: RAG with OpenAI GPT Models
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- Overview: RAG with OpenAI GPT Models (4:34)
- Case Study Briefing: Cooking Books (4:57)
- Converting PDF to Images (9:15)
- Reading a Single Image with GPT (12:03)
- Enhancing AI with Prompt Engineering (9:10)
- Reading All Images in a Dataset (5:07)
- Filtering Non-relevant Information (6:03)
- Understanding Embeddings in NLP (6:50)
- Generating Embeddings (13:56)
- Building FAISS Index and Metadata Integration (6:27)
- Implementing a Robust Retrieval System (14:41)
- Combining Outputs for Enhanced Results (2:56)
- Constructing a Generative Model (11:42)
- Complete RAG System Implementation (6:41)
- How to Improve RAG Systems Effectively? (7:03)
Section 11: Working With Unstructured Data
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- Overview: Working With Unstructured Data (3:36)
- Introduction to Langchain Library (7:26)
- Excel Data: Best Practices for Data Handling (6:41)
- Python - Initial Setup for Data Processing (5:47)
- Loading Data and Implementing Chunking Strategies (5:13)
- Developing a Retrieval System for Unstructured Data (6:10)
- Building a Generation System for Dynamic Content (9:12)
- Building Retrieval and Generation Functions (9:57)
- Working with Word Documents (4:54)
- Setting Up Word Documents for RAG (6:17)
- Implementing RAG for Word Documents (2:26)
- Working with PowerPoint Presentations (4:44)
- PowerPoint Setup for RAG (4:11)
- RAG Implementation for PowerPoint (3:09)
- Working with EPUB Files (4:58)
- EPUB Setup for RAG (4:47)
- RAG Implementation for EPUB Files (2:22)
- Working with PDF Files (4:21)
- PDF Setup for RAG (5:51)
- RAG Implementation for PDF Files (5:37)
- What Did You Learn in This Section? (3:56)
- Exercise: Imposter Syndrome (2:55)
Section 12: Multimodal RAG
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- Overview: Multimodal RAG (3:38)
- Introduction to Multimodal RAG (5:58)
- Setup and Video Processing (5:23)
- Extracting Audio from Video (8:44)
- Compressing Audio Files (4:17)
- Transcribing Audio with OpenAI Whisper (10:07)
- Whisper Model (6:31)
- Extracting Frames from Video (5:49)
- Introduction to Contrastive Learning (5:14)
- Understanding the CLIP Model (5:22)
- Tokenizing Text for Multimodal Tasks (8:13)
- Chunking and Embedding Text (11:36)
- Embedding Images for Multimodal Analysis (8:36)
- Understanding Cosine Similarity in Multimodal Contexts (6:46)
- Applying Contrastive Learning and Cosine Similarity (10:26)
- Visualizing Text and Image Embeddings (11:11)
- Query Embedding Techniques (4:12)
- Calculating Cosine Similarity for Query and Text (11:47)
- GenAI Model Setup for Multimodal Tasks (4:55)
- Building a GenAI Model (7:11)
- What Did You Learn in This Section? (2:12)
Section 13: Project - Starbucks Financial Data
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days
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Section 14: RAG with OpenAI File Search
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Section 15: Agentic RAG
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- Overview: Agentic RAG (2:51)
- AI Agents (7:51)
- Agentic RAG (5:44)
- Setup and Data Loading (9:54)
- State Management and Memory in Agentic Systems (7:54)
- AgentState Class (4:29)
- Greeting the Customer (4:52)
- AI Agent that Checks the Question (10:47)
- AI Agent that Assesses the Validity of the question (7:22)
- Retrieving the Documents (5:46)
- Testing the App (7:13)
- Generate Answers (9:21)
- AI Agent that Improves the Answer (11:13)
- Asking User For More Questions (5:29)
- Agentic RAG Recap - Key Learnings and Next Steps (6:17)
Section 16: The Science of RAG
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Section 17: Knowledge Graphs with LightRAG
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days
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- Game Plan for Knowledge Graphs with LightRAG (2:19)
- Knowledge Graphs (7:19)
- Knowledge Graphs vs Embeddings (8:49)
- LightRAG library update (April 2025)
- LightRAG Setup (7:35)
- What is LightRAG? (4:40)
- Setting the Working Directory (2:28)
- Data Prep (4:48)
- Naive RAG (6:07)
- Implementing LightRAG (6:10)
- Knowledge Graph Visualization (8:20)
- Local Knowledge Graph Visualization (6:15)
Section 18: RAGAS
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after you enroll
- Game Plan for RAGAS (1:53)
- Assessing RAG with RAGAS (6:13)
- RAGAS Setup (3:50)
- Embedding and Facebook AI Similarity Search (FAISS) (9:51)
- Python - RAG (11:37)
- Synthetic Data (3:36)
- Generating Synthetic Data (4:59)
- Python - Answering Synthetic Dataset (6:28)
- ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score (5:32)
- ROUGE (13:49)
- LLM-Based Assessment (6:07)
- Simple Criteria Score - Part 1 (5:35)
- Simple Criteria Score - Part 2 (5:39)
- Factual Correctness (5:16)
- Rubrics Score (4:52)
- Semantic Similarity (4:46)
- Factual Correctness (4:57)
- Context Precision (3:12)
- Semantic Similarity (6:21)
- Context Recall (3:11)
- Context Precision (5:57)
- Response Relevancy (4:36)
- Context Recall (4:55)
- Response Relevancy (6:22)
- Key Learnings and Outcomes: RAGAS (3:17)
Where To Go From Here?
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