The End of Search Engines? RAG Explained (Full 8-Hour Course)

Master Search and RAG with this complete course by Isaac Flath. From keyword search to multimodal embeddings, learn how to build modern AI search systems hands-on.

Learn Retrieval Augmented Generation - https://www.boot.dev/courses/learn-retrieval-augmented-generation

Code "isaac" for 25% off.

Go check out Isaac's channel! (https://www.youtube.com/@isaac-flath)


CHAPTERS:

00:00 - Ch1 L1 - Retrieval Augmented Generation
01:29 - Ch1 L2 - What is search
05:41 - Ch1 L3 - Project Overview
07:52 - Ch1 L4 - Keyword Search
15:52 - Ch1 L5 - Text Processing
18:28 - Ch1 L6 - Punctuation
20:59 - Ch1 L7 - Tokenization
25:52 - Ch1 L8 - Stop words
33:35 - Ch1 L9 - Stemming
37:26 - Ch2 L1 - Inverted Index
45:52 - Ch2 L2 - Use the index
50:38 - Ch2 L3 - Boolean Search
52:05 - Ch2 L4 - Term Frequency
01:02:46 - Ch2 L5 - Inverse Document Frequency
01:08:06 - Ch2 L6 - TF-IDF
01:11:21 - Ch3 L1 - Keyword Search
01:18:58 - Ch3 L2 - Term Frequency Saturation
01:25:03 - Ch3 L3 - Document Length Normalization
01:36:00 - Ch3 L4 - BM25 Search
01:47:03 - Ch4 L1 - Semantic Search
01:50:23 - Ch4 L2 - Embeddings
01:52:04 - Ch4 L3 - Embedding Models
01:56:41 - Ch4 L4 - Model Selection
01:59:38 - Ch4 L5 - Vector Operations
02:02:31 - Ch4 L6 - Dimensions
02:04:22 - Ch4 L7 - Dot Product Similarity
02:06:18 - Ch4 L8 - Why Cosine Similarity?
02:10:54 - Ch4 L9 - Why Cosine Similarity
02:16:46 - Ch4 L11 - Document Embeddings
02:26:21 - Ch4 L12 - Query Embeddings
02:29:51 - Ch4 L13 - Same Model
02:32:17 - Ch4 L14 - Implementing Semantic Search
02:40:18 - Ch4 L15 - Locally-Sensitive Hashing
02:42:00 - Ch4 L16 - Vector Databases
02:44:14 - Ch5 L1 - Chunking
02:51:17 - Ch5 L2 - Chunk Overlap
02:57:17 - Ch5 Challenge - Semantic search basics
02:58:47 - Ch5 L3 - Semantic Chunking
03:06:03 - Ch5 L4 - Chunked Semantic Embeddings
03:19:49 - Ch5 L5 - Chunked Semantic Search
03:34:07 - Ch5 L6 - Chunked Edge Cases
03:37:47 - Ch5 L7 - ColBERT
03:41:03 - Ch5 L8 - Late Chunking
03:43:19 - Ch6 L1 - Keyword vs Semantic
03:44:50 - Ch6 L2 - Hybrid Search
03:48:53 - Ch6 L3 - Score Normalization
03:55:56 - Ch6 L4 - Weighted Combination
04:14:01 - Ch6 L5 - Reciprocal Rank Fusion
04:25:50 - Ch7 L1 - Large Language Models
04:27:29 - Ch7 L2 - Gemini API Setup
04:32:42 - Ch7 L3 - Spell Correction
04:40:59 - Ch7 L4 - Query Rewriting
04:43:35 - Ch7 L5 - Query Expansion
04:47:22 - Ch8 L1 - Re-Ranking
04:50:33 - Ch8 L2 - LLMs for Re-Ranking
05:05:45 - Ch8 L3 - LLM Batch Re-Ranking
05:18:41 - Ch8 L4 - Cross-Encoder Re-Ranking
05:25:38 - Ch9 L1 - Manual Evaluation
05:29:04 - Ch9 L2 - Golden Dataset
05:32:50 - Ch9 L3 - Precision Metrics
05:41:48 - Ch9 L4 - Recall Metrics
05:52:28 - Ch9 L5 - F1 Score
05:58:16 - Ch9 L6 - Error Analysis
06:12:46 - Ch9 L7 - LLM Evaluation
06:24:41 - Ch9 Challenge - Designing Chunking Strategy (Interview)
06:29:05 - Ch9 Challenge - Query -- Document Embeddings
06:30:14 - Ch10 L1 - Augmented Generation
06:40:20 - Ch10 L2 - LLM Summarization
06:47:34 - Ch10 L3 - Conflict Resolution in Summaries
06:49:33 - Ch10 L4 - Adding Citations
06:56:20 - Ch10 L5 - Question Answering
06:59:22 - Ch11 L1 - Recursive Rag
07:01:19 - Ch11 L2 - Agentic Search
07:03:12 - Ch12 L1 - Multimodal Search
07:11:19 - Ch12 L2 - Multimodal Embeddings
07:14:17 - Ch12 L3 - Image Embeddings
07:20:45 - Ch12 L4 - Multimodal Search Implementation

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