Ai Engineering Fundamentals

This archive groups articles that answer one recurring problem area instead of dumping generic news. Each post is written to be extractable by both humans and answer engines.

ai-engineering-fundamentals

AI Roadmaps Need Kill Criteria, Not Just Launch Dates

A roadmap without kill criteria assumes every AI feature deserves to survive. That is how weak ideas consume months of engineering time.

2026-04-25 · makeyourAI.work

ai-engineering-fundamentals

A One-Model MVP Is Usually Better Than a Clever Multi-Agent Demo

The first production question is not how many agents you can orchestrate. It is whether one model can solve one valuable problem reliably enough to matter.

2026-04-21 · makeyourAI.work

ai-engineering-fundamentals

Human Review Loops Are a Scaling Mechanism, Not a Temporary Crutch

Human review does not mean the AI system failed. It often means the team understands where automation should stop.

2026-04-20 · makeyourAI.work

ai-engineering-fundamentals

Fine-Tuning Is Usually the Wrong Answer Before You Fix Your Interface

Fine-tuning can be powerful, but it often gets blamed for problems that actually belong to prompt design, retrieval quality, or product constraints.

2026-04-17 · makeyourAI.work

ai-engineering-fundamentals

AI Product Specs Need Failure Modes, Not Just User Stories

A user story tells you what the person wants. An AI product spec must also describe how the system behaves when probability does not cooperate.

2026-04-16 · makeyourAI.work

ai-engineering-fundamentals

AI Engineer Learning Paths Are Broken Without Product Pressure

The fastest way to waste months learning AI is to study abstractions without having to ship anything that can break.

2026-04-11 · makeyourAI.work