What Is AI Engineering?
6 min read
How AI engineering differs from ML research and traditional software, and what the job actually involves.
AI engineering is the discipline of building reliable products on top of foundation models. Where ML research focuses on training new models, AI engineering focuses on composing existing models with retrieval, tools, evaluation, and guardrails into systems users can depend on.
What the job involves
- Designing prompts, context, and retrieval so the model has what it needs
- Wiring models to tools and data with function calling
- Evaluating quality and catching regressions before users do
- Operating systems in production: cost, latency, and reliability
tip
You rarely need to train a model. Most production wins come from better context, retrieval, evaluation, and orchestration.
A minimal example
resp = client.responses.create(
model="gpt-4o",
input="Summarize the incident report in 3 bullets.",
)
print(resp.output_text)From this starting point, production systems add retrieval for grounding, tools for actions, evaluation for quality, and monitoring for reliability — the topics in this track.