18 Concepts to Get Good at AI Engineering in 2026
Author: Neo Kim — System Design One Source: LinkedIn
If you want to get good at AI engineering (in 2026), then learn these concepts.
The 18 Concepts
| # | Concept |
|---|---|
| 1 | LLM Evals Explained |
| 2 | Design Knowledge Q&A System |
| 3 | How OpenClaw Works |
| 4 | AI Agent Workflow |
| 5 | How MCP Works |
| 6 | Design AI Chat Assistant |
| 7 | How RAG Works |
| 8 | Agentic Patterns Explained |
| 9 | AI Coding Workflow 101 |
| 10 | Machine Learning System Design 101 |
| 11 | Multi-Agent Architecture Explained |
| 12 | How AI Agents Work |
| 13 | How Vector Databases Work |
| 14 | AI Agents: Memory, State & Consistency |
| 15 | AI Agents Design |
| 16 | Context Engineering Fundamentals |
| 17 | What is Reinforcement Learning |
| 18 | LLM Concepts — A Deep Dive |
Notable Comments
Worth adding model selection and cost optimization. Understanding when to route between open and closed models based on task complexity can cut inference costs by 80%.
Context Engineering is quickly becoming one of the most underrated skills in AI. The quality of the context often matters as much as the model itself.
Prompt Caching and Cost Optimization — the best AI systems aren’t just accurate, they’re efficient and economically scalable.
Add Prompt Engineering, Model Context Windows, and AI Safety & Guardrails. Building AI systems is as much about reliability as capability.
About Neo Kim
Runs System Design One newsletter (201K+ subscribers). Produces system design and AI engineering content with detailed visual explanations.
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