AI Engineer’s Field Guide
A practical playbook for designing production AI systems
What it does
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.
Does the same job
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Hello HN! My name is Max, and I’m a co-founder at Lynx (https://uselynx.ai). We’re building an AI-powered incident resolution platform to help engineers debug and resolve on-call issues faster. If you’ve ever been paged in the middle of the night and had to spend hours piecing together logs, metrics, and code, we’d love your feedback. * The Problem * On-call hasn’t kept pace with modern engineering. Even with great observability tools, diagnosing incidents is slow because: - Systems are increasingly complex. - Logs, dashboards, and documentation are scattered. - Context often…
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