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AI Engineer’s Field Guide
A practical playbook for designing production AI systems
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.
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.
About AI Engineer’s Field Guide on Product Hunt
“A practical playbook for designing production AI systems”
AI Engineer’s Field Guide was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #141 on the daily leaderboard. 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.
On the analytics side, AI Engineer’s Field Guide competes within Developer Tools, Artificial Intelligence and Data Science — topics that collectively have 992.4k followers on Product Hunt. The dashboard above tracks how AI Engineer’s Field Guide performed against the three products that launched closest to it on the same day.
Who hunted AI Engineer’s Field Guide?
AI Engineer’s Field Guide was hunted by Sanjay G. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AI Engineer’s Field Guide including community comment highlights and product details, visit the product overview.
Sanjay G