LLM Evaluation Journal: treating quality as a product metric

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: we can ship quickly but still lose reliability when ownership stays fuzzy.

Instead of adding more moving parts, I tested a short feedback loop with measurable quality gates.

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 replaced a vague process step with a concrete, testable checkpoint.
• I documented one decision that usually lives in hallway conversations.

The practical lesson

The immediate gain was fewer surprises; the bigger gain is compounding trust. Across these projects, clarity in operating rules keeps outcomes stable under pressure.

Tomorrow’s focus

Tomorrow I will review this with the team so the decision is shared, not personal.

References

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