Stop Debating—Start Measuring: Practical LLM Eval Loops
Cross-Industry • ~8–9 min read • Updated Jul 25, 2025
Context
Opinions don’t ship. Evals do. Teams stall in model debates because evidence is thin and inconsistent across functions. A minimal, portable evaluation loop builds a common language for quality and risk so decisions move faster—and safer.
The Minimal Eval Loop
- Define Outcomes: Choose 3–5 task-level KPIs (answerability, factuality, safety, latency, cost).
- Build Golden Sets: Curate 50–200 real artifacts per task (tickets, emails, forms). Mask sensitive data.
- Create Rubrics: Write plain-language criteria with 0–3 scoring and examples of each level.
- Error Taxonomy: Group misses into buckets (hallucination, policy breach, retrieval miss, formatting, refusal).
- Automate Harness: One command to run models/prompts/tools against goldens; store traces + scores.
- Gate with Thresholds: Define “ship” lines per KPI and require regression checks on change.
How to Make It Stick
- Single Source of Truth: Version prompts, policies, and goldens together.
- Weekly Eval: Run on a schedule; track score deltas and cost/latency.
- Red/Amber/Green Views: Roll up by task and by error type to focus fixes.
- Attach to Governance: Evals are inputs to change control and incident review.
Common Pitfalls
- Goldens from Toy Data: Use production-like artifacts; otherwise scores won’t transfer.
- Binary Pass/Fail: Lose signal—use rubrics to see progression.
- No Trace Capture: Without inputs/outputs/prompts, you can’t reproduce or learn.
Quick Start Checklist
- Pick one task and curate 100 goldens from real work.
- Draft a 0–3 rubric for answerability and policy fit.
- Stand up a simple harness that logs inputs, outputs, scores, and cost.
Closing
With portable goldens, clear rubrics, and a tiny harness, evals become a habit—not a fire drill. That habit replaces opinion loops with evidence and lets you ship safely, often.