AI Systems

This page is for the production stack view of AI engineering: the pieces that turn a model demo into a reliable system. The goal is to explain how to think about architecture, memory, orchestration, observability, evaluation, deployment, and trust boundaries without turning the repo into a giant platform.

Back to LearningAI Explore the stack

Production AI stack

What belongs in a real AI system

Use this as a companion to the article you referenced. The page should emphasize the production concerns senior engineers need to internalize.

1. Model interface

Prompting, tool calls, response shaping, and fallback behavior. This is the visible edge of the system, but not the whole system.

2. Context and memory

RAG, vector search, document stores, summaries, and long-lived project memory. The key question is what should be retrieved versus compiled.

3. Orchestration

Single-agent flows, multi-agent workflows, approval gates, retries, handoffs, and durable state. Claude Code / agent runtimes fit here.

4. Observability

Tracing, event IDs, logs, token usage, latency, quality signals, and debugging breadcrumbs. If you can’t inspect it, you can’t trust it.

5. Evaluation

Golden sets, task-specific checks, regressions, human review, and cost/performance tradeoffs. A good demo that can’t be measured is not production-ready.

6. Deployment and ops

Config, versioning, secrets, runtime limits, monitoring, and safe rollouts. The last mile is where most AI systems become real software.

Suggested page structure

How to organize the content in LearningAI

Opening section

What an AI system is
Why demos fail in production
How the stack fits together

Core sections

Model + prompt interface
Memory / RAG / knowledge layer
Agentic workflow + orchestration
Evaluation and observability
Deployment and guardrails

What to include from the article

Production stack framing
Claude Code as the developer workflow layer
Persistent memory and project context
Token budgeting and reliability tradeoffs

What to keep opinionated

Don’t over-index on buzzwords
Prefer durable system thinking over prompt tricks
Use real production examples whenever possible

Next action

Add this page to the LearningAI home screen and wire it to a guide/notebook pair.

The cleanest version is probably: one HTML landing page, one topic guide page, and one notebook or demo folder for practical exercises.