LLM Cost and Latency Notes: using caching where it actually pays off

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: teams over-rotate on tooling when alignment is the real bottleneck.

Instead of adding more moving parts, I tested an explicit contract for inputs, outputs, and owners.

By May, the quality of data and AI foundations shows up clearly in delivery speed.

What I changed today

• I aligned a technical decision with a business-facing success metric.
• I reduced unnecessary variability by standardizing one recurring pattern.
• I documented one decision that usually lives in hallway conversations.

Why this mattered today

Nothing looked flashy, but the system became easier to reason about under pressure. Most of the win comes from making ownership and boundaries unmistakably clear.

Tomorrow’s focus

Tomorrow I want to tighten the metrics so improvements are obvious without interpretation.

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