Case Study · Evaluation

RAG Evaluation Lab

Question datasets, automatic scoring, and regression tests that block releases when quality drops after updates.

ProblemTeams ship updates without knowing if answer quality regressed.

SolutionAutomated eval harness with scorecards and pass/fail thresholds.

ImpactSafer iteration cycle and fewer production quality surprises.

Architecture

Evaluation Pipeline

Designed as a release-control system: dataset contracts, deterministic scoring, drift diagnostics, and explicit merge gates.

dataset registry -> run orchestrator -> metric scorers -> drift analyzer
         |                    |                  |               |
         v                    v                  v               v
  case stratification   model/retriever      quality deltas   gate policy
   + gold citations      variant matrix      + CI trendline   + release decision
                                         |
                                         v
                                  report + artifacts

Why this matters: evaluations become enforceable release policy instead of passive dashboards.

Dataset Registry

Versioned benchmark questions with tags, gold answers, and required citation sources.

Scoring + Diagnostics

Faithfulness, relevance, citation precision/recall, latency, and failure-mode classification per case.

Release Gate Engine

Regression thresholds and quality floors decide pass/fail for merge and deploy pipelines.

Evidence

Release Gate Policy

Accuracy Delta

Must not fall below previous stable release threshold.

Faithfulness

Source-supported claims checked with automatic validators.

Regression Score

Composite quality gate for merge approval in CI.

Testing Strategy

Nightly benchmark runs for major datasets.
PR-level smoke eval for fast feedback.
Canary eval against real anonymized queries before rollout.

Presentation path: projects/rag-evaluation-lab/presentations/upcoming/

Technical Peek

Policy-Driven Evaluation Orchestrator

@dataclass
class GatePolicy:
    max_quality_regression: float = -0.02
    min_pass_rate: float = 0.90
    min_faithfulness: float = 0.88
    max_p95_latency_ms: int = 1900


def evaluate_and_gate(
    run_ctx: RunContext,
    cases: Sequence[EvalCase],
    client: RAGClient,
    baselines: BaselineStore,
    policy: GatePolicy,
) -> tuple[EvalSummary, GateDecision]:
    baseline = baselines.get_baseline(run_ctx.suite_name)
    results = [evaluate_case(client, case, run_ctx=run_ctx) for case in cases]
    summary = summarize_results(run_ctx=run_ctx, results=results, baseline=baseline)
    decision = apply_release_gate(summary=summary, policy=policy, baseline=baseline)

    if not decision.allow_release:
        raise RuntimeError("Evaluation gate failed: release blocked by policy.")

    return summary, decision

Why this matters: this gate prevents silent quality regressions from reaching production after prompt/model/index changes.

Advanced Breakdown

Most Important Engineering Decisions

1. Versioned Benchmark Registry

Every evaluation suite is versioned with controlled question sets and expected behavior so score changes are compared against a stable reference instead of moving targets.

Why this matters: metric movement is interpretable and release decisions are reproducible.

Benchmark Integrity

2. Multi-Metric Scoring Strategy

Accuracy, faithfulness, citation match, and latency are scored together with weighted summaries, preventing optimization on one metric while silently degrading others.

Why this matters: quality gates reflect production reality, not a single narrow KPI.

Balanced Quality

3. Hard Regression Gates in CI

Pull requests fail automatically when regression deltas cross tolerated thresholds, forcing fixes before merge rather than relying on post-release monitoring.

Why this matters: known quality drops never ship by accident.

Release Safety

4. Layered Eval Cadence

Fast PR smoke tests run on representative subsets while nightly full-suite runs cover broader edge cases and trend drift, keeping feedback both quick and deep.

Why this matters: teams keep development speed without sacrificing rigor.

Developer Velocity

5. Historical Drift Reporting

Eval runs are persisted and compared over time to surface long-horizon degradation, not only immediate PR-to-PR changes.

Why this matters: slow quality decay is detected early before user trust drops.

Observability