Most AI content shows tools and APIs. These series focus on something slightly different: why the patterns exist, what problem they solve, where they break, and how to think through the engineering decisions behind them.
Newest
- RAG in Practice — Part 4: Chunking, Retrieval, and the Decisions That Break RAG
- Part 5: Build a RAG System from Scratch (publishing soon)
Choose a Path
MCP in Practice
How AI applications connect to tools, data, and external systems — from first principles to local builds to production concerns.
You'll leave knowing: why connecting AI to systems is harder than it looks, what MCP actually standardizes, and how to build and harden a working MCP server.
Four waypoints through the series:
- Part 1 — Why connecting AI to real systems is still hard
- Part 5 — Build your first MCP server (and client)
- Part 6 — Your MCP server worked locally. What changes in production?
- Part 9 — From concepts to a hands-on example
RAG in Practice
How retrieval-augmented generation actually works, where it fails, and how to build and reason about it step by step.
You'll leave knowing: why RAG exists, what chunking and retrieval actually decide, and how to build a working pipeline from scratch.
Four waypoints through the series:
- Part 1 — Why AI gets things wrong
- Part 3 — How RAG works: the complete pipeline
- Part 4 — Chunking, retrieval, and the decisions that break RAG
- Part 5 — Build a RAG system from scratch (publishing soon)
Where to Start
New here? → MCP Part 1 or RAG Part 1
Want to build something? → MCP Part 5 or RAG Part 5 (publishing soon)
Care about the decisions? → MCP Part 4 or RAG Part 4
If this kind of practical AI writing is useful to you, this page is the easiest way to see what exists.
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