How LLMs Actually Work: A Mental Model in 4 Steps
LLMs don't understand your text. They predict tokens. Here's the 4-step mental model that explains hallucinations, context costs, and why prompts work.
Prompt Injection: How LLMs Can Be Tricked (and Defend)
Prompt injection is the SQL injection of the LLM era. Here's how attackers slip instructions into your model, why it's hard to fix, and what reduces risk.
ChatGPT vs Gemini: An Honest Side-by-Side for Learners
ChatGPT and Gemini compared for AI learners in 2026: context window, reasoning, coding, pricing, and which to start with.
Vector Databases Explained: Why LLM Apps Need Them
Vector databases find semantically similar text using embeddings. Here's how they work, why SQL can't do this, and which one to pick for your LLM app.
What is Prompt Engineering? A Hands-On Guide
Prompt engineering is how you get reliable, useful outputs from LLMs. Here's what it means, the 5 building blocks, and what breaks when you skip them.
What is RAG? Retrieval-Augmented Generation Explained Simply
RAG gives LLMs access to knowledge they weren't trained on. Here's how retrieval-augmented generation works, what breaks, and when to build one.