AI Engineer’s Field Guide
A practical playbook for designing production AI systems
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Sanjay G

AI Engineer’s Field Guide - A practical playbook for designing production AI systems

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Most AI system designs fail before the first model call because engineers pick a model or vector DB before framing the business decision. This Field Guide flips that: a top-down method mapping any problem onto 5 architecture pillars (Data, Intelligence, Orchestration, Guardrails, UX). Includes decision trees (RAG vs fine-tuning, agents vs single call, chunking), a phased build roadmap with cloud mappings, and a 10-incident production playbook. Interactive HTML + offline PDF.

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Sanjay G
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I built this because I was intimidated. I'd spent years on model training and writing production-grade code, but never actually deployed an AI system. And the gap felt huge. Making an API call and getting a response is nowhere close to a real application. When I went looking for help, resources were scattered, and a lot of LinkedIn content just showed off "here's a problem, here's my solution" without ever sharing the underlying framework you could reuse for any problem. So I built the framework I wished existed: a top-down method, a 5-pillar architecture model, decision trees for the forks I kept hitting, and an incident playbook for the failures I actually ran into. If you've felt that same intimidation moving from "I can train a model" to "I can build a system" then this is for you. Would love your feedback.