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RIP-04: Evaluations & Quality Control
Specifies how clients anonymize and randomize evaluation submissions to mimic normal inference requests.
Goals
- Prevent provider bias by making evals indistinguishable from real inference calls.
- Collect unbiased metrics on quality, latency, and cost.
Eval Flow
- Client selects a subset of providers based on discovery.
- For each eval job:
- Generate randomized inference parameters.
- Send inference request randomized with proxy.
- Measure latency, output quality against ground truth.
- Assemble eval result and prepare Nostr event:
- Use new random ephemeral Nostr key per eval.
- Publish Kind 31555 with standard rating tags:
d: provider node-idratingcategories:quality,latency,value
- No direct link to client's main pubkey.
Tags
d: provider node-idrating:quality: 0–1 match to ground truthlatency: normalized inverse latency scorevalue: cost vs performance
content: optional detailed notes
Anonymity
- Ephemeral keys rotated per session.
- Randomized send times & intervals.
Aggregation
- Clients can batch multiple evaluations in one event by repeating
ratingtags.
Frequency
- Limit eval jobs to ≤5% of overall requests to avoid load spikes.