·AI & ChatGPT
Module 2 Lesson 1: How ChatGPT Processes Prompts
Understanding the technical journey of a prompt from text input to numerical processing and back to text.
embeddings
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·Artificial Intelligence
Module 3 Lesson 3: Embeddings in Practice
How are businesses actually using those big lists of numbers? In our final lesson of Module 3, we look at semantic search, RECOMMENDATIONS, and the basics of RAG.
·Artificial Intelligence
Module 3 Lesson 2: How Embeddings Are Learned
Embeddings aren't created by humans; they are learned by machines. In this lesson, we look at the intuition behind how LLMs build their conceptual map of the world.
·Artificial Intelligence
Module 3 Lesson 1: What Embeddings Are
How does a computer know that a 'King' is like a 'Queen' but not like a 'Kilometer'? In this lesson, we explore Embeddings: the mathematical heart of AI meaning.
·AWS Bedrock
Module 7 Lesson 2: Chunking and Embeddings
Slicing the Data. Understanding how Bedrock breaks docs into 'Chunks' and turns them into 'Vectors' of meaning.
·Generative AI
Module 2 Lesson 2: How LLMs Work (Conceptual)
From Tokens to Embeddings. Understanding the mechanics of how a computer 'reads' meaning.
·Generative AI
Module 5 Lesson 2: Vector Databases
The Memory of AI. Understanding how we store and search 'Meaning' using embeddings and specialized databases.
·LangChain
Module 6 Lesson 1: What are Embeddings?
The Math of Meaning. How to turn human words into a list of numbers that represent their semantic soul.
·LangChain
Module 6 Lesson 2: Embedding Providers (Cloud vs. Local)
Choosing your engine. Comparing OpenAI cloud embeddings with local HuggingFace models for speed and privacy.
·LangChain
Module 6 Wrap-up: Storing Your AI's Memory
Hands-on: Build a local knowledge base using ChromaDB and perform semantic queries.
·AI & LLMs
Module 10 Lesson 2: Embeddings with Local Models
Turning words into math. Understanding the 'Embeddings' that power local semantic search.