Foundations

Core concepts that form the bedrock of everything else in this knowledge base. Whether you’re evaluating AI for your organization or building with it daily, these fundamentals inform every decision.

Start here: How LLMs Work gives you the mental model. Then read Tokens & Pricing to understand costs. Everything else builds on these two.

Pages in This Section

How LLMs Work

What large language models are, how they learn, and why they sometimes get things wrong. The essential mental model.

Tokens & Pricing

How AI processes text, what it costs, and strategies to optimize spending. Includes real pricing tables.

Choosing a Model

Model selection framework: capability vs. cost vs. context window. Covers the 2025-2026 landscape.

Open vs Closed Source

Privacy, cost, customization, and vendor lock-in. A complete decision guide with comparison tables.

Deployment Models

Cloud vs. on-premise: where to run AI, when each model wins, and how to deploy with Docker.

AI Agents Explained

What AI agents are, how they work, types of agents, and the agentic patterns shaping 2025-2026.

Memory & RAG

How agents remember: the four memory types, vector databases, embeddings, and retrieval-augmented generation.

Glossary

A-Z reference of AI terms used throughout this knowledge base.

Suggested Learning Path

1

Start: How LLMs Work

Understand the engine. What LLMs are, how they predict text, and why hallucination happens.
2

Then: Tokens & Pricing

Learn the unit of everything — cost, limits, and performance all trace back to tokens.
3

Next: Choosing a Model

Apply the selection framework: match the right model to your specific use case and budget.
4

Deep Dive: Memory & RAG

Understand how agents gain long-term memory — the technology behind intelligent assistants.