Optimizing Cost, Latency, and Performance

The earlier lessons in this chapter built the ability to measure whether a system works, compare and iterate on it, and diagnose exactly why something went wrong. This lesson turns to a related but distinct question. Once a system is correct, how do you make it efficient, and keep it efficient, as it runs at real volume over time. Optimization draws on the same observability infrastructure built earlier in this course, but it asks that data a different question, not whether something broke, but where cost, latency, and token usage are being spent without earning their keep. In this lesson, we will cover:

• Why optimization is a distinct discipline that follows correctness, not a substitute for establishing it

• Reading the same logging and tracing data built earlier for tuning opportunities, not only regression detection

• The token-level, call-level, and architecture-level places cost, and latency actually accumulate

• Weighing a batch discount against a real deadline with actual numbers, rather than a habit of processing everything the same way

Optimization follows correctness, it does not replace it

A component that produces a wrong answer faster and more cheaply has not improved. It has made the same problem harder to notice. Cutting reasoning effort or retrieval depth before a component’s accuracy has actually been evaluated risks locking in a real quality problem underneath a lower cost, and any optimization made this way needs to be re-verified against the same evaluation dataset that established the baseline, the identical discipline covered earlier for any other change to a system, ...