Learning Hub
Learning Hub is structured, foundational content for architects and technology leaders: AI concepts explained for architects, architecture fundamentals, learning paths, framework comparisons, and a working glossary. The goal is not to turn every architect into an ML researcher — it's to learn enough to understand, design, evaluate, and lead.
Path 1: The AI Architect's Blueprint
A five-part foundation series, in order — start here if you're building AI architecture knowledge from the ground up.
- LLM & Generative AI Foundations
- RAG Systems & Vector Databases
- Agentic AI: Agents, MCP & Multi-Agent Systems
- Enterprise AI Architecture & LLMOps
- AI Governance, Risk & Domain Applications
Coming Soon
More learning paths, standalone explainers, and comparisons are in development and will publish on a rolling basis:
Additional Learning Paths
Follow-on paths for after you've completed Path 1.
Path 2: AI Platform Engineering Fundamentals
The operational layer, explained conceptually — gateways, LLMOps, and observability, and why each one exists, before you ever open the Playbooks.
Path 3: AI Governance & Risk Fundamentals
The regulatory and compliance landscape for AI, explained for architects — risk classification, accountability, and the frameworks shaping enterprise AI policy.
Concept Explainers
Short, standalone "what is X" pieces — the fastest way to get oriented on one idea at a time.
What Is an AI Agent?
Agents vs. chatbots vs. workflows — what actually makes something "agentic," and where the term gets misused.
Tokens, Context Windows & Embeddings, Explained
The three concepts every architect needs before any of the rest of this site makes sense.
What Is Model Context Protocol (MCP)?
A plain-language explanation of MCP, what problem it solves, and how it's different from a plain API integration.
Fine-Tuning vs. RAG vs. Prompt Engineering
Three different ways to customize a model's behavior, and how to tell which one you actually need.
What Is an AI Gateway?
Why enterprises are putting a gateway in front of every model call, and what it actually does.
SLMs vs. LLMs: When On-Prem/Edge Actually Makes Sense
Why smaller, cheaper models running on-prem or at the edge are winning specific use cases away from large cloud LLMs.
Framework & Tool Comparisons
Side-by-side educational comparisons — lighter than a formal vendor-selection process.
Agent Framework Comparison: LangChain vs. LlamaIndex vs. Semantic Kernel vs. AutoGen
A side-by-side look at how the major agent frameworks differ, without the vendor-selection scorecard treatment.
Vector Database Comparison: Pinecone vs. Weaviate vs. pgvector vs. Milvus
What actually differs between the major vector database options, in plain terms.
Glossary
A single, living reference — not a series, just kept up to date.
The AI Architecture Glossary
A living reference of AI and architecture terms used across this site, added to over time.
Coming Soon
More learning paths, standalone explainers, and comparisons are in development and will publish on a rolling basis:
Additional Learning Paths
Follow-on paths for after you've completed Path 1.
Path 2: AI Platform Engineering Fundamentals
The operational layer, explained conceptually — gateways, LLMOps, and observability, and why each one exists, before you ever open the Playbooks.
Path 3: AI Governance & Risk Fundamentals
The regulatory and compliance landscape for AI, explained for architects — risk classification, accountability, and the frameworks shaping enterprise AI policy.
Concept Explainers
Short, standalone "what is X" pieces — the fastest way to get oriented on one idea at a time.
What Is an AI Agent?
Agents vs. chatbots vs. workflows — what actually makes something "agentic," and where the term gets misused.
Tokens, Context Windows & Embeddings, Explained
The three concepts every architect needs before any of the rest of this site makes sense.
What Is Model Context Protocol (MCP)?
A plain-language explanation of MCP, what problem it solves, and how it's different from a plain API integration.
Fine-Tuning vs. RAG vs. Prompt Engineering
Three different ways to customize a model's behavior, and how to tell which one you actually need.
What Is an AI Gateway?
Why enterprises are putting a gateway in front of every model call, and what it actually does.
AI Pair Programming vs. Agentic Coding: A Spectrum
Where autocomplete, Copilot-style suggestions, and autonomous coding agents each sit — and what changes as autonomy increases.
Context Engineering for Code
What an AI coding tool actually needs to see — repo context, specs, style guides — to produce usable output, and why prompting for code differs from prompting for prose.
Framework & Tool Comparisons
Side-by-side educational comparisons — lighter than a formal vendor-selection process.
Agent Framework Comparison: LangChain vs. LlamaIndex vs. Semantic Kernel vs. AutoGen
A side-by-side look at how the major agent frameworks differ, without the vendor-selection scorecard treatment.
Vector Database Comparison: Pinecone vs. Weaviate vs. pgvector vs. Milvus
What actually differs between the major vector database options, in plain terms.
AI Coding Assistant Landscape
Copilot, Cursor, Claude Code, Windsurf, and similar tools compared on autonomy level, IDE integration, and enterprise controls.
AI Code Review Tool Comparison
Where automated review tools fit alongside human review, and what they catch versus miss.
Glossary
A single, living reference — not a series, just kept up to date.
The AI Architecture Glossary
A living reference of AI and architecture terms used across this site, added to over time.
AI-Native Development Practices
Practical guidance for working with AI day to day in the engineering workflow.
Best Practices for Reviewing AI-Generated Code
What to check for that differs from reviewing human-written code.
Best Practices for Working with Coding Agents
Scoping tasks, giving feedback, and knowing when to take back manual control.