What to learn first

The fastest path is learning to decompose a workflow and connect the right data. Learn where judgment is needed, then evaluate whether the system made the business process better. Model-release news can wait.

  • Start with workflows and the decisions users make.
  • Design data access around retrieval, with permissions and source visibility handled up front.
  • Use tool calling only where deterministic systems are better than text output.
  • Evaluate on task success, with latency and cost tracked, and back it with regression checks and human review.
  • Deploy with observability and rollback paths, under 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, and keeps doing so safely.
  • 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.