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Eval Drift — Guardrails and Fix Pattern

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Evaluation disclaimer (RAG drift)
Drift signals here are measured inside specific RAG pipelines and datasets.
They are debugging indicators, not proof that a system will stay stable in all real workloads.

When evaluation metrics swing unpredictably across runs (precision, recall, ΔS, coverage) even though the data and index appear unchanged.
This signals eval drift: your evaluation harness is not structurally stable.

Open these first

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45 across 3 paraphrases
  • Coverage ≥ 0.70 per target section
  • λ convergent on 2 seeds, stable across runs
  • Variance of metrics ≤ 0.05 across replays

Typical symptoms → exact fix

Symptom Likely cause Open this
Precision/recall varies ±0.20 each run eval harness non-deterministic Eval Precision/Recall
Identical queries give different metrics bootstrap not fenced Bootstrap Ordering
Eval metrics collapse on fresh deploy index not fully warmed Predeploy Collapse
Coverage < 0.50 despite gold answers embedding or chunk drift Embedding ≠ Semantic, Chunking Checklist

Fix in 60 seconds

  1. Lock seeds
    Fix random seeds at retrieval, reranker, and eval harness layers.

  2. Fence bootstrap
    Require VECTOR_READY==true and index hash match before eval begins.

  3. Replay 3 paraphrases
    Eval the same question with 3 paraphrases. Require ΔS variance < 0.05.

  4. Cross-seed check
    Run two seeds. λ must remain convergent across both.

  5. Regression gate
    Ship only if coverage ≥ 0.70 and precision/recall stable within 0.05.

Copy-paste eval harness snippet

def eval_guardrails(question, retrieved, gold):
    ds_qr = deltaS(question, retrieved)
    ds_rg = deltaS(retrieved, gold)

    assert ds_qr <= 0.45, "ΔS drift detected"
    assert coverage(retrieved, gold) >= 0.70, "Coverage too low"
    assert lambda_state(retrieved) in {"→","←","<>"} , "λ divergent"

    return {
        "ΔS_qr": ds_qr,
        "ΔS_rg": ds_rg,
        "coverage": coverage(retrieved, gold),
        "λ": lambda_state(retrieved)
    }

Diagnostic probes

  • Re-run variance test: run eval 5 times, log precision/recall. Stddev >0.05 → unstable harness.
  • Anchor comparison: compare ΔS to gold anchor vs decoy. If both similar, re-embed.
  • Deploy warm-up: log VECTOR_READY and index hash before eval.

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