The AI Engineering Loop is how teams approach the continuous evolution and improvement of their AI-powered systems. It connects what happens in production directly to the work of improving quality, cost, latency, and reliability during development. (View Highlight)
Many of the underlying concepts mirror traditional software engineering, but a key differentiator is the probabilistic nature of LLM outputs and the sheer number of paths a system can take. You cannot unit-test your way to confidence. You need a systematic way to observe, learn, and improve via experiments. (View Highlight)
The first part is about visibility. What is your system actually doing in the real world? Which requests are going well, and which are failing in ways that matter?
Capture the full path of a request, including prompts, retrieved context, tool calls, outputs, latency, and cost. Tracing is the raw record of what your system actually did. (View Highlight)
Track how the system behaves over time and surface the traces that deserve attention. Monitoring turns a stream of raw data into an ongoing understanding of how the system evolves. Evaluation methods help you surface quality over time and draw attention to interesting events in your application. Implicit and explicit user feedback, along with cost or latency anomalies, help you surface interesting traces (View Highlight)
The second part is about turning what you have observed into improvements you can trust — without degrading the parts of the system that are already working. If your application is not in production, datasets, experiments, and evaluation are a great starting point for gaining confidence in your system before deploying to production. (View Highlight)
Turn real scenarios surfaced through monitoring and expected scenarios you design during development into repeatable test cases. Instead of testing against a handful of hand-picked examples, you build a set that reflects how the system actually gets used. A dataset can contain examples from production as well as hypothetical examples that define the surface area your system will fac (View Highlight)
Change variables systematically — a prompt, a model, a retrieval strategy — and compare each change against a stable baseline or other experimental setups. That way you know what actually improved instead of guessing. (View Highlight)
Decide whether results are good enough to ship using manual review, code-based checks, or LLM-as-a-judge. Evaluation is how you turn a comparison into a decision. (View Highlight)
Once you ship a change, the cycle starts again. The updated system produces new traces, new monitoring signals, and new opportunities to improve. (View Highlight)
The value of the loop is cumulative. Each step you add gives you better signal, more systematic coverage, and more confidence in what you are shipping. The goal is not to implement everything at once — it is to understand where you are and take the next step toward closing the loop. Many teams start with tracing or by building early datasets. (View Highlight)
One natural place to begin is tracing. You cannot monitor what you cannot see, and you cannot improve what you cannot measure. Tracing is the foundation everything else builds on. Let’s assume your starting point is an application that has been running live for some months. You now want to better understand what the system actually does step by step, as a foundation for evaluating and improving your system. Adding tracing will be a great starting point to gain those insights. (View Highlight)