From Prototype to Production: The AI Engineer Field Guide

Building a slick AI prototype in a notebook is easy. Putting it into production is where the pain starts.

Are you struggling to bridge the gap between a basic LLM script and a robust, integrated system?

Are you lost in the noise of vector DBs, chunking strategies, and tiered model routing?

Do hallucinations, latency spikes, and runaway cloud costs make you hesitant to deploy?

Have you mastered training models, but architectural system design still feels like a guessing game?

If you nodded to even one of these, this field guide is your blueprint.

What You’ll Learn

This guide skips the "Hello World" tutorial. It provides a structured, top-down mental model to design end-to-end AI systems with absolute confidence.

End-to-End Thinking: Architect complete systems, not just isolated models.

Definitive Decision Making: Clear rules on RAG vs. Fine-Tuning, Agents vs. Single LLM calls, and which chunking strategy actually works for your document type.

Orchestration & Agents: Design workflows that allow AI to safely plan, call tools, and execute multi-step tasks without infinite loops.

Production Ops & Cost Control: Design with latency, token budgets, and LLM-as-a-judge evaluations as primary constraints.

What Makes This Different?

This is not a 300-page textbook of theory. It is a highly tactical, interactive framework used in real-world production environments. You’ll get access to:

The Pathfinder: An interactive tool to pinpoint exactly what you are building (RAG, Agents, Classifiers) and highlight the exact architectural path you need.

The 5-Pillar Architecture: A clean framework mapping your project across Data Foundation, Intelligence Core, Orchestration, Guardrails, and UX.

The Incident Playbook (NEW): A structured troubleshooting guide. What do you do when your AI confidently hallucinates in production? Or when latency spikes? Get the root cause, diagnosis, and exact fix for the most common production incidents.

Interactive Build Checklist: A phased, trackable checklist with specific tips for Azure, AWS, and GCP to ensure you never miss a critical production guardrail.

Deep-Dive Case Study: A complete architectural walkthrough of an AI-Powered Claims Processing Assistant handling 200,000+ documents across a global workforce.

The Cloud Map: A direct translation layer mapping architectural components to their exact Azure, AWS, and GCP service equivalents.

What’s in the Bundle?

The Interactive HTML Version (The Gold Standard): A fully responsive, dark-mode-ready, interactive application. Click through decision trees, track your checklist progress, filter the incident playbook dynamically, and jump through cross-linked architectural concepts.

The Premium PDF Version: A beautifully formatted, dark-themed summary guide designed for quick reference, offline reading, and executive sharing.

Who Is This For?

Data Scientists executing a strategic pivot into Applied AI Engineering.

Software & ML Engineers struggling to make RAG or Agentic systems reliable at scale.

Tech Leads & Architects who need to map complex business problems to technical GenAI stacks.

Any Gen AI Enthusiast who is ready to level up and go beyond theory to building end-to-end AI systems.

Why This Matters

Most AI projects don't fail because the model is weak; they fail because the system design is fragile. Stop guessing your way through architecture. Build grounded, cost-effective, and production-ready AI systems today.---© 2026 Sanjay G. All rights reserved.