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Live webinarSystem DesignAI EngineeringArchitecture
Design a production AI system from scratch
The diagram you want to reach for when your demo is suddenly real traffic.
Param Harrison
Cofounder, AEOsome.com · Chief Mentor, learnwithparam.com
60 minutes · intermediate
About this session
Why this one matters
Most AI demos never survive contact with real users. The prompt works in a notebook, the retrieval falls apart on a fresh document, costs spike on the first real traffic week. This session walks the full design of a production AI system: where the LLM sits, what wraps it, how data moves in and out, where you measure what breaks. You leave with a diagram you can draw on a whiteboard and a list of decisions you can defend in design review.
Who should watch
- Backend engineers shipping their first production AI feature this quarter
- Tech leads owning the design review for an AI service
- Engineers with a working demo and no idea what production actually needs
Topics we'll cover
What's on the menu
- Where the LLM actually sits in a production system
- The data plane: ingress, retrieval, grounding, and egress
- Caching, rate limits, and cost control before real traffic hits
- Observability for non-deterministic systems
- Eval loops that catch regressions across releases
- The whiteboard diagram you can defend in design review
What you'll walk away with
Leave with a blueprint
- Draw the full shape of a production AI system, not just the prompt layer
- Place retrieval, tools, and evals where they actually live under load
- Design cost and latency controls into the system from day one
- Decide where to measure, what to log, and how to catch silent regressions
- Walk into your next design review with a defensible architecture