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AI Literacy
Plain-English guides to understanding AI without a programming background.
What AI, machine learning, and neural networks actually are, how ChatGPT works, why AI makes mistakes, what agents can and cannot be trusted to do, and how to use AI tools with judgment at work. No code required.
37 posts, from beginner to advanced. Each ends with what to read next.
AI Agents: What They Are, What They Can Do, and How They Go Wrong
An agent is an AI that takes actions, not just answers questions. That changes what safe use looks like. Learn in plain English what agents are, how they connect to your tools, why they can be tricked by what they read, and the one question to ask before letting one act for you.
Capstone: Build, Document, and Present an AI-Powered Project
The capstone brings everything together. You will build a real AI-powered project, evaluate it systematically, document it clearly, and present it to a non-technical audience.
Career Paths Into AI (Technical and Non-Technical)
Map the AI-related roles, what each one expects, and which next step fits your current background.
Product Decisions About AI: Quality, Cost, Latency, Risk
Every AI feature involves tradeoffs between quality, cost, latency, and risk. This module gives you a framework for making and defending those decisions.
Reading an ML System Design Document
ML system design documents describe how AI systems are built, evaluated, and operated. This module teaches you to read and understand them without an engineering background.
How to Work With ML Engineers and Data Scientists
What do ML engineers and data scientists actually do? This module explains their roles, how they think, and how to communicate with them as a non-technical collaborator.
Evaluating What You Built: Does It Actually Work?
Building something is only half the work. This module covers how to evaluate an AI application systematically - before and after deployment.
Automation With AI: Connecting Tools and Workflows
AI tools become much more powerful when connected to each other and to your existing workflows. This module covers the patterns and platforms that make this possible.
RAG Without the Code: What It Is and How to Use It
Retrieval-Augmented Generation is the pattern behind most enterprise AI tools. This module explains what it is, why it works, and how to use it without building it from scratch.
Building Your First AI Application With APIs
You do not need to train a model to build an AI application. This module walks through building a working LLM-powered tool using an API - step by step.
Visualization: Turning Numbers Into Decisions
A well-chosen chart communicates what a table cannot. This module covers the visualizations that matter for data exploration and how to choose among them.
What Makes Data Useful for ML?
Not all data trains good models. This module explains what properties of a dataset determine whether ML can learn from it - and what to look for before starting a project.
Reading and Exploring a Dataset in Python
Load a real dataset, understand what is in it, and answer questions about it - step by step, with no prior Python knowledge assumed.
What Is Data? Rows, Columns, Types, and Missing Values
Data is not just numbers in a spreadsheet. Understanding structure, types, and quality is the foundation for everything else in data work and ML.
Python for Non-Programmers: The Absolute Minimum
You do not need to become a software engineer to use Python for data work. This module covers only what you need: variables, lists, functions, and running a script.
Evaluating AI Tool Output: A Practical Framework
Most people use AI tools without any systematic evaluation. This module gives you a practical framework for assessing output quality, factual accuracy, and fitness for purpose.
AI for Research, Writing, and Analysis
AI tools can compress hours of research, first-draft writing, and data summarization into minutes. This module covers the workflows that work and the failure modes to watch for.
Working With AI Coding Agents Effectively
AI coding tools no longer just suggest a line; they take a task, edit files, run commands, and report back. Learn how to delegate to a coding agent, review what it did, and stay in control without writing code yourself.
When to Trust an AI Output (and When Not To)
AI tools produce confident-sounding output regardless of accuracy. This module gives you a practical framework for deciding when to trust, verify, or reject an AI response.
Prompt Engineering That Actually Works
Learn the prompt patterns that help more often than they hurt, and how to tell when the output still needs checking.
What Data Actually Does in an AI System
Data is not just fuel for AI - it determines what an AI system can and cannot do. This module explains the relationship between data quality and model quality.
Large Language Models: Under the Hood, in Plain English
How do GPT, Claude, and Gemini actually generate text? No math required - just the accurate mental model you need to use and evaluate them well.
What Neural Networks Are (and Aren't)
Neural networks are not digital brains. They are function approximators. This module explains what that means and why it matters.
How Machine Learning Differs from Normal Software
Traditional software follows explicit rules. Machine learning infers rules from examples. Understanding this difference changes how you think about every AI system.
What Is AI, Really? Stripping Away the Hype
A clear explanation of what artificial intelligence is, what it is not, and why the hype often gets in the way.
Responsible AI: Bias, Fairness, and What Teams Should Do
Responsible AI is practical product discipline: define risks, measure harms, review data, monitor behavior, and design human accountability.
The 10 Things AI Cannot Do Yet
AI is powerful, but it has real limits. This post explains ten important things current AI systems cannot reliably do and how to plan around them.
Prompt Engineering: What It Is and Why It Matters
Prompt engineering is the practice of giving AI systems clear context, constraints, examples, and evaluation criteria so outputs become more useful.
What Is a Vector Database?
Vector databases store embeddings so systems can search by meaning instead of exact words. This guide explains why they matter for modern AI products.
How Recommendation Systems Work
Recommendation systems power feeds, playlists, shopping pages, and search results. This post explains the basic mechanics from data to ranking.
What Is Overfitting? And Why You Should Care
Overfitting is when a model memorizes the past instead of learning patterns that work in the future. This guide explains it with practical examples.
What Is a Training Dataset and Why Does It Matter?
Training data teaches a model what patterns to learn. This guide explains examples, labels, quality, leakage, and why data choices shape AI behavior.
Why AI Makes Mistakes: Hallucinations, Bias, and Limits
AI mistakes are not random magic. They come from data, objectives, missing context, and system design choices. Here is how to think about them.
What Is a Neural Network? An Honest Explanation
Neural networks are not digital brains, but they are powerful pattern learners. This post explains them honestly with the right amount of intuition.
The Difference Between AI, ML, and Deep Learning
AI, machine learning, and deep learning are related but not interchangeable. This guide gives you a clean mental map for the three terms.
How ChatGPT Works Without the Jargon
ChatGPT predicts and composes text using a large language model trained on patterns in language. Here is the practical mental model without the technical fog.
What Is Machine Learning? A Plain English Guide
Machine learning is software that improves by learning patterns from examples. This guide explains the idea without jargon and shows where it works, where it fails, and why data matters.