u/Artikku

Hi everyone, this is my first time starting an e-commerce business, and I want to sell stationery. I used the Accio Sourcing Toolkit to filter out a group of suppliers on Alibaba that meet my needs. However, why are there different unit prices and minimum order quantities for the same product images? How can I find the actual factories that manufacture this product? My workflow used to require separate tools for verification, but Accio Work automatically checks third-party data for legal litigation and risk records during the background check phase, which helps me immediately identify if they are a risky trading company. Besides excluding new accounts and suppliers without addresses, what other factors can I use to determine their products? Thanks for your advice.

ran into this last week and curious how others handle. we use llm-as-judge with gpt-4o-2024-08-06 pinned as our judge model. eval gate runs as a github actions step on every PR touching prompts or agent code. gate blocks merge if pass rate regresses >5% vs a rolling 30-day baseline (computed nightly).

openai deprecated gpt-4o-2024-08-06 last week (the usual "supported until X" notice). we migrated to gpt-4o-2024-11-20. our gate immediately started flagging PRs as regressions even when the underlying code was unchanged. pass rates dropped ~7% across the board on the new judge. PRs that were green 3 weeks ago are now failing on the same eval set.

the new judge isn't worse on absolute correctness. it's just calibrated differently. different propensity to flag borderline cases. different confidence in the "pass" decision. our 5% threshold doesn't know about this calibration shift.

options i'm weighing:

1. rebaseline everything when the judge changes. clean but loses ~2-3 weeks of comparable historical signal.
2. dual-judge during transition (1-2 weeks of both judges in parallel, weighted average). expensive (2x judge cost) and the weighting logic is brittle.
3. judge-specific baselines (separate baseline per (judge_model, rubric_version) tuple). cleanest analytically but real operational overhead at our scale.
4. abstraction wrapper that calibrates outputs against a fixed reference set before scoring. most upfront engineering work but stable downstream across judge changes.

leaning toward 3 but the operational overhead is concerning. we run ~12 PR-blocking eval runs per day across 4 agent products, each ~200 cases × 3 samples for variance. so ~14k judge calls/day. baseline maintenance at that volume isn't trivial.

is there a fifth option i'm missing? specifically interested in how teams running LLM eval gates at meaningful scale (not toy projects) handle judge model deprecations, because openai and anthropic both ship 3-4 judge-capable snapshots per year and this is going to keep happening.

also: have any of you actually validated that the new judge is more correct on your domain before adopting it, or do you trust openai's "this snapshot is better" claims? we didn't validate and that might have been the mistake.