How RAG fails in production, and how to fix it
The follow-up to the RAG explainer. The four ways RAG breaks in production (chunking, retrieval, faithfulness, a stale index), the fixes that hold, and the testing that shows them.
Writing / Section
AI basics
Plain-English explainers of how the technology works. 7 articles, newest first.
What is RAG, really?
RAG from scratch: how chunk, embed, store, retrieve, augment and generate fit together, why it cuts hallucination without ending it, and where it quietly breaks. About 13 minutes.
What is a context window, really?
A plain explainer of the context window: the shared token budget a model reads at once, why long context costs more and recalls less, and when retrieval beats a bigger window. About 12 minutes.
What is agentic AI? A no-hype guide
Agentic AI plans and carries out multi-step tasks on its own. It decides what to do, uses tools, and keeps going until the goal is met. A clear guide to what really ships as an agent in 2026.
What is an embedding, really?
A from-scratch guide to what embeddings are, how they're compared, why they make modern search work, and which model to pick in 2026. About 15 minutes.
MCP explained: the protocol wiring AI into everything
The Model Context Protocol went from a quiet Anthropic release to industry-standard plumbing in under two years. Here's what it is, why every major lab adopted it, and where the hype got ahead of the facts.
What is an LLM, really?
A from-scratch guide to how large language models work: tokens, attention, the prediction loop, and what to make of it all. 12 minutes.