Sampled Prompt Trace Eval
Log every production trace but run LLM-judge evaluation on a configurable sample — keep quality metrics tracking real traffic without doubling inference costs at scale.
Intent & Description
🎯 Intent
Capture full prompt/response/metadata traces from production but run LLM-judge evaluation on a random sample only — so monitoring cost stays bounded as traffic grows.
📋 Context
A production LLM application receives thousands or millions of requests. You want production quality metrics on actual traffic, not just offline eval sets. Running an LLM judge on every request doubles inference cost and is infeasible at scale.
💡 Solution
Log every production request’s prompt, response, retrieved context, model parameters, and metadata to a monitoring store (Opik, LangSmith, Comet). On a configurable sample rate (e.g. 5% uniform plus 50% on enterprise tenants), run the LLM judge against the rubric. Aggregate scores over time windows. Surface drift in dashboards. Sampling rate, weighted slices, and budget are all configuration.
Real-world Use Case
- Production traffic is large enough that judging every trace is infeasible.
- Drift detection on real traffic matters — offline eval sets aren’t enough.
- Some slices (e.g. enterprise tenants, high-value queries) justify weighted sampling.
Source
📌 TL;DR
Log every trace, judge a sample — quality metrics on real production traffic without 2× inference costs. Tune sampling rates per slice to catch what matters.
Advantages
- Monitoring cost stays bounded as traffic grows — sample rate controls spend.
- Quality metrics track production distribution, not just offline benchmark sets.
- Drift detection is statistically defensible with proper sampling design.
Disadvantages
- Tail-end rare failures may be under-sampled and invisible in dashboards.
- Sampling rate tuning is a recurring decision as traffic patterns change.
- Slice-weighted sampling adds complexity to dashboards and drift attribution.