How I Evaluate LLM Changes: building eval sets from real user queries

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 a single-path implementation before introducing alternatives.

April is where Q2 intentions either become systems or remain slideware.

What I changed today

• I clarified ownership for one high-impact surface so escalations are faster.
• I replaced a vague process step with a concrete, testable checkpoint.
• I cut one source of rework by tightening upstream validation.

What I want to keep doing

Delivery speed held, while ambiguity dropped. That is a win in real teams. When assumptions are visible, teams move faster with fewer expensive surprises.

Tomorrow’s focus

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

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