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
Field Notes The machine
The machine
writes in public.
Clear explanations of prompting, retrieval, model interfaces, deployment boundaries, and operational rigor.
Retrieval Trust Boundaries Matter More Than Vector Search Hype
The hard part of retrieval is not only finding text. It is deciding which text deserves to influence the answer and under what conditions.
2026-04-24 · makeyourAI.work
MCP Is Not the Product, Operating Rules Around It Are
Protocols create possibility. Products are created by boundaries, permissions, evaluation, and task design.
2026-04-23 · makeyourAI.work
Structured Output Is Product Design, Not Prompt Decoration
The moment another system needs to consume model output, structure stops being optional. It becomes part of the product contract.
2026-04-22 · makeyourAI.work
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
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
Tool Use Without Auth Boundaries Is Just Prompted Overreach
The model should not be trusted simply because the prompt says to behave. Permissions belong to systems, not to wishes.
2026-04-19 · makeyourAI.work
If You Cannot Trace an LLM Failure, You Do Not Really Have a Product
When an LLM feature fails and no one can reconstruct what happened, the product is operating on vibes instead of evidence.
2026-04-18 · makeyourAI.work
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 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
Model Routing Only Helps When the Product Contract Is Stable
Routing between models is useful only after you know what the task is, how quality is measured, and where the real variance comes from.
2026-04-15 · makeyourAI.work
Context Windows Are a Budget, Not a License to Dump Everything In
A larger context window does not make irrelevant information harmless. It just makes expensive confusion easier to hide.
2026-04-14 · makeyourAI.work
Build Evals Before You Build Agents
An agent without evals is not a system with autonomy. It is a system with excuses.
2026-04-13 · makeyourAI.work
AI Features Fail When Nobody Writes Acceptance Criteria for Model Behavior
If your AI feature has no acceptance criteria, you are not shipping software. You are releasing a probability distribution and hoping the user forgives you.
2026-04-12 · makeyourAI.work
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
Prompting as Interface Design, Not Magic Text
Prompting gets better the moment you stop treating it like clever phrasing and start treating it like interface design.
2026-04-10 · makeyourAI.work
When RAG Is the Wrong Answer
Many teams reach for RAG because it sounds like the modern answer, not because retrieval is actually the cleanest way to solve the product problem.
2026-04-09 · makeyourAI.work
What MCP Is in Practice, Not Just in Demos
MCP matters because it turns ad hoc tool wiring into a clearer contract, but the protocol only helps if your surrounding system is disciplined.
2026-04-08 · makeyourAI.work