DAY 1: LLM & RAG Foundations: Transformers, Embeddings, and Vector Databases Explained Simply

Description

Want to understand the technology powering modern AI assistants like GPT-4 and Llama? This video breaks down the essential architectural components and processes you need to master: Transformers, Attention, Context Windows, Embeddings, and Vector Databases. In this foundational deep dive, we explain key concepts in plain English: 1. The Transformer Backbone The Transformer is a neural network architecture, introduced in 2017, that serves as the backbone of modern LLMs. Its primary job is to efficiently process sequences of data, such as text, by understanding the relationship between all elements in the sequence, regardless of how far apart they are. Unlike older models (RNNs/LSTMs) that processed text word-by-word and often "forgot" the start of long sentences, the Transformer processes all words simultaneously. 2. Attention and Context The Attention Mechanism is a mathematical formula that allows the model to calculate how much "focus" or "importance" to place on every other word in the input sequence when processing the current word, ensuring the model understands context and relationships. The Context Window is the maximum number of tokens an LLM can look at, remember, and process at one time. We explain the critical implication for RAG: if your prompt plus retrieved text is larger than this window, the model will truncate the input, potentially losing important information. 3. Embeddings and Similarity We simplify Embedding Models (like Sentence Transformers), which convert human-readable text strings into a list of numbers called a vector. The goal is to represent the meaning of the text in a mathematical space, following the key principle that meaningful text is close together in vector space. We also define Cosine Similarity, which measures the angle between two vectors (not their length) to determine how similar their meanings are. A small angle (Cosine near 1) means the texts have very similar meaning. 4. Vector Databases & Chunking Learn why Vector Databases are specialized and essential: they are designed for extremely fast storage and querying of high-dimensional vectors. While standard databases find exact matches, vector databases are crucial for RAG because they are fast at finding approximate nearest neighbors (ANN)—the vectors closest to your query vector. Finally, we cover Chunking, which is the process of breaking down large documents into smaller, manageable, and semantically coherent blocks. This is necessary because you cannot embed a 50-page document into one vector without losing detail, nor can you feed 50 pages into an LLM’s context window. Each small chunk is turned into its own vector and stored, ensuring the LLM receives highly relevant, focused information that fits the context window. Watch now to master the vocabulary required to build and optimize Retrieval-Augmented Generation (RAG) systems!