What to learn first
The fastest path is not memorizing every model release. It is learning how to decompose a workflow, identify where judgment is needed, connect the right data, and evaluate whether the system made the business process better.
- Start with workflows and user decisions, not model demos.
- Design data access with retrieval, permissions, and source visibility.
- Use tool calling only where deterministic systems are better than text output.
- Evaluate with task success, latency, cost, regression checks, and human review.
- Deploy with observability, rollback paths, and clear ownership.
AI engineering vs ML engineering
| Discipline | Primary question | Typical output |
|---|---|---|
| ML engineering | Can we train, serve, and monitor a model? | Models, pipelines, feature stores, model services. |
| AI engineering | Can we make an AI workflow useful and reliable? | Agents, tools, evals, interfaces, workflow automation. |
| Forward deployed engineering | Can we make it work inside a specific customer context? | Integrated systems, playbooks, production deployments. |
Glossary
- AI engineering
- The discipline of turning AI capabilities into reliable product and workflow systems.
- Agent
- An AI system that can use tools, follow a goal, and make bounded decisions across steps.
- Evaluation
- A repeatable test of whether an AI system completes the task correctly, safely, and consistently.
- Retrieval
- The process of giving a model relevant external context from documents, databases, or APIs.
- Human in the loop
- A control pattern where people approve, correct, or review AI output before high-impact actions.