Hey — welcome to Production AI Notes. Every week: one concept, one architecture, one project idea, one interview question. Built for developers who want to ship real AI systems. Let's go.
One concept: what an AI engineer actually does
An AI engineer builds software that uses models as components. Not training foundation models — applying them well. Day to day: retrieval pipelines (RAG), agents that use tools safely, evals, observability, cost/latency, and deployment.
If you already write software, this is the shortest path into AI — you're adding a new kind of component to skills you already have.
One architecture: the demo vs the system
A demo is one layer (UI → model). A production system adds retrieval, evals, observability, controls, and safety. Same idea, many more layers — and those layers are the job.
Full breakdown: Production-Ready GenAI Architecture
One project idea
Build a minimal RAG service this week: ingest 20 docs, chunk them, embed, retrieve top-k, and answer with citations. Put it on GitHub with a README. That's your Stage 02.
One interview question
"Your RAG app returns confident but wrong answers. How do you debug it?"
Strong answer: start with retrieval, not the model. Check whether the right chunks are being retrieved at all (log retrieved context). Then look at chunking, reranking, and grounding. Only after that, prompt and model.
That's it. Reply and tell me what you're building — I read everything.
— Gururaj Opinions my own. Public/synthetic examples only. · Get the roadmap