AI, LLMs, RAG & Agents
AI, LLMs & RAG
From neural network intuition to production retrieval-augmented generation and agents.
Helpful before you begin (optional -- you can start this course now): Python Fundamentals
Who it's for
Developers who can program and want to build grounded, cited AI applications rather than only calling a chat API.
What you'll be able to do
- Distinguish AI, machine learning, deep learning, and generative AI precisely
- Explain how tokens, context windows, and embeddings shape what a model can do
- Build a retrieval-augmented generation pipeline with chunking, search, and citations
- Evaluate answer quality and defend against prompt injection and data leakage
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What these systems actually are
Separating the vocabulary, then building intuition for how models work.
- 20 min
AI vs Machine Learning vs Deep Learning vs Generative AI
Untangle four terms that get used interchangeably but mean different things.
- 22 min
Neural Network Intuition
How a neural network turns numbers into predictions, without the heavy math.
- 24 min
Transformer Intuition, Tokens, and Context Windows
Why order matters to language models, and how they actually 'see' text.
- 24 min
Prompt Design: Instructions and Structured Outputs
Write prompts that reliably get the response shape and quality you actually need.
Retrieval
Turning documents into something a model can search.
- 26 min
Embeddings and Vector Similarity
How meaning gets turned into numbers you can compare mathematically.
- 24 min
Chunking and Document Ingestion
Split documents into retrieval-sized pieces before they can be searched or embedded.
- 24 min
Semantic, Keyword, and Hybrid Search
Compare meaning-based search, exact-word search, and combining the two.
Retrieval-augmented generation
Grounding answers in real sources, and proving they are grounded.
- 28 min
Retrieval-Augmented Generation: The Full Pipeline
Connect retrieval and generation into one system that answers from your own documents.
- 22 min
Reranking and Citations
Improve retrieval order and make every claim traceable back to its source.
- 26 min
Reducing Hallucination and Evaluating RAG Quality
Build honesty checks into a RAG system and measure whether it's actually working.
Agents and production concerns
Letting a model act, safely, at acceptable cost.
- 26 min
Prompt Injection and Data Privacy
Defend a RAG system against instructions hidden in retrieved content, and protect sensitive data.
- 26 min
Tool and Function Calling
Let a model request actions from your code, instead of only producing text.
- 26 min
AI Agents and Workflows
Chain multiple tool calls into a bounded plan-act-observe loop.
- 28 min
Cost, Latency, Caching, Observability, and Production Safeguards
The operational concerns that turn a working AI prototype into a reliable product feature.