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Building Your Own Model Evaluation: Shadow Testing & LLM-as-Judge
Public benchmarks can mislead, as the Intermediate level covered. The fix isn’t to find a better leaderboard — it’s to build your own evaluation, on your own task, and roll out a new model without betting production on an untested guess.
The Quick Answer
Start with 10–20 real examples from your own domain, run every candidate model against them, and score the outputs — by hand for a small set, or with another model acting as an automated judge (LLM-as-judge) once you need to scale evaluation up. Before a new model ever reaches real users, run it in shadow mode alongside the current one on live traffic, comparing outputs without exposing anything — this catches regressions that an offline test set alone would miss. And when a bigger model does win, calculate whether the quality gain is actually worth its extra resource cost, rather than assuming bigger always means better.
Step 1: Build a Task-Specific Evaluation Set
Collect 10–20 real, representative examples of the exact task you need the model to do — actual support tickets, actual code review requests, actual documents your system will really encounter. This directly closes the gap the Intermediate level exposed: a benchmark is a stand-in for your task; a set built from your task is your task. Run each candidate model against every example, and score the output — correct/incorrect for objective tasks, or a quality rating for subjective ones like tone or writing style.
Step 2: Scale Scoring With LLM-as-Judge
Manually scoring 15 examples is manageable. Scoring hundreds or thousands isn’t — which is where LLM-as-judge comes in: another model (often a stronger one) evaluates each output against criteria you define in plain language, effectively automating the manual review step. This works especially well as a pairwise judge — shown two candidate outputs side by side and asked to pick the better one — which tends to be more reliable than asking a single model to assign an absolute quality score on its own.
Step 3: Shadow Test Before You Switch
An evaluation set — no matter how well built — is still a fixed, offline snapshot. Real production traffic is messier and shifts over time in ways a static test set can’t fully anticipate. Shadow testing closes that gap: the new model runs on real, live traffic in parallel with the current one, but its output is only logged and compared, never actually shown to users. This surfaces regressions the offline evaluation missed, with zero risk to anyone using the system. Once shadow results look solid, a staged rollout — a small percentage of real traffic first, with the ability to roll back immediately, before a full switch — is the standard way production teams de-risk the final step.
Step 4: Is the Upgrade Actually Worth It?
A bigger model scoring higher on your own evaluation still costs more to run — more memory, more compute, often less throughput. The same discipline covered in Cloud AI vs Local AI’s cost calculator applies here too: calculate the actual quality gain per unit of extra resource cost, rather than assuming a bigger model is automatically the right call.
Fun Fact
Production teams running high-stakes model rollouts often shadow-test for one to two weeks, then move to a small canary rollout — as little as 5% of real traffic — with an immediate rollback plan in place, before ever reaching 100%. The full rollout window for a high-stakes model swap can stretch to several weeks, entirely to avoid betting everything on a single evaluation snapshot.
Test Yourself
What is the main advantage of building your own evaluation set from real task examples?
What is "shadow testing" when rolling out a new model?
What is "LLM-as-judge"?
In the cost-per-quality-point calculation, what does a lower "cost per accuracy point" mean?