LLM Cost and Latency Notes: reducing token waste without hurting answer quality

I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight integration tests.

The friction I kept seeing was simple: performance conversations are often really architecture conversations.

Instead of adding more moving parts, I tested a review pass focused on maintainability over novelty.

March for me has been about tightening execution after an idea-heavy February.

What I changed today

• I replaced a vague process step with a concrete, testable checkpoint.
• I reduced unnecessary variability by standardizing one recurring pattern.
• I cut one source of rework by tightening upstream validation.

What I want to keep doing

I came away convinced that constraint clarity beats optimization tricks most days. Good systems feel calm because decision paths are explicit before incidents happen.

Tomorrow’s focus

Tomorrow I want to verify this pattern under a busier workload before I call it stable.

References

• Microsoft Foundry documentation
• RAG design and evaluation guide
• Azure Well-Architected for AI workloads\n\n## Takeaways\n\nAdd a concise, personal takeaway and recommended next steps here.\n