AI and GTM concepts in plain English: from LLMs to SDRs.
8 articles · Updated
The intermediate companion to our RAG explainer: the four ways RAG degrades in production (chunking, retrieval, faithfulness, a stale index), the fixes that hold, and the evaluation that makes them visible.
A from-scratch explainer of RAG: how chunk, embed, store, retrieve, augment, and generate fit together, why it reduces hallucination without ending it, and where it quietly breaks, in about 13 minutes.
A from-scratch 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, in about 12 minutes.
Most AI agent pilots stall before production: no owned metric, no workflow redesign, no kill criterion. McKinsey shows 62% experimenting but 23% scaling. The five-question gate to run first.
Agentic AI plans and executes multi-step tasks on its own: it decides what to do, calls tools, and loops until the goal is met. A clear guide to what actually ships as an agent in 2026.
A from-scratch explainer of what embeddings actually are, how they're compared, why they make modern search possible, and which model to pick in 2026, in about 15 minutes.
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 outran the facts.
A from-scratch explainer of how large language models actually work (tokens, attention, the inference loop, and what to make of it all) in 12 minutes.
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