How I Evaluate LLM Changes: tracking groundedness before celebrating fluency

I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.

The friction I kept seeing was simple: quality regressions are expensive because they are discovered too late.

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

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 cut one source of rework by tightening upstream validation.
• I clarified ownership for one high-impact surface so escalations are faster.

Why this mattered today

The work felt less heroic and more repeatable, which is exactly the direction I want. The repeated lesson for me is that explicit design intent creates durable speed.

Tomorrow’s focus

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

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