Evaluating LLM Outputs Without Going Bankrupt

Automated evaluation is the single highest-leverage investment a GenAI team can make — and the easiest to get wrong. Run every eval through GPT-4 as a judge and your eval budget eclipses your inference budget within a week. Here is how we keep eval costs sane at Loop Commerce.

Stratify your eval set

Not every test case needs the expensive judge. Stratify inputs into three tiers:

• Tier 1 — heuristic checks. Regex, exact match, length bounds, JSON schema validity. Runs on every case, costs nothing.
• Tier 2 — embedding similarity. Compare output to a reference set with cosine similarity. Cheap, catches semantic drift.
• Tier 3 — LLM-as-judge. Reserved for the ambiguous 5–10% where heuristics and embeddings are insufficient.

Sample, do not exhaust

You do not need to eval all 10,000 cases on every PR. Sample 300 stratified cases for routine checks; run the full suite on model swaps and weekly cron. You catch regressions either way, at a fraction of the cost.

Cache judge responses

LLM-as-judge results for a given (input, output, rubric) tuple are deterministic enough to cache. Store the verdict keyed by a hash of the inputs. On re-runs you skip the judge call entirely.

Track eval cost as a first-class metric

If eval spend is invisible, it grows until it hurts. Surface it next to inference cost on your dashboard. The moment a team sees eval tokens outpacing production tokens, the stratification work happens naturally.

Good evaluation is not about running the most powerful judge on the most cases. It is about running the cheapest judge that catches the regression you care about on the fewest cases that still represent your traffic.