Relevance feedback: Evaluation and operations

Defines datasets, metrics, judgments, experiments, monitoring, diagnostics, and acceptance thresholds for relevance feedback. It fixes adoption boundaries and observable failure evidence while keeping environment-specific limits configurable.

Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.

Decision

For Relevance feedback, adopt a versioned relevance contract evaluated on representative judgments. Treat the block as a release and regression contract built from representative positive, boundary, denial, failure, and recovery cases. This testing block focuses specifically on evaluation and operations. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.

Scope

• The Relevance feedback actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes required for the evaluation and operations focus of this testing block.
• Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
• Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.

Outside this block

• Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
• Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.

Contract

• Define query interpretation, candidate eligibility, ranking signals, tie-breaking, filters, and fallback independently of one search engine.
• Apply tenant, entitlement, visibility, and deletion rules before returning hits, snippets, suggestions, facets, or total counts.
• Version source records, index documents, analyzers, ranking configuration, and evaluation sets so changes are attributable.
• Measure quality by intent segment and protect latency, error, freshness, and zero-result guardrails during relevance experiments.
• Record fixture version, environment, configuration, observed result, and evidence location for every decision-bearing run.
• Separate product failure, dependency failure, test-infrastructure failure, and inconclusive evidence in reports.
• Separate explicit judgments, clicks, skips, reformulations, conversions, complaints, and editorial labels and retain their context, bias, consent, and collection policy version.
• Use feedback as weighted evidence rather than truth, protect against manipulation and popularity loops, and validate ranking changes on fixed offline sets plus online guardrails.
• For the evaluation and operations variant, enforce this declared boundary: defines datasets, metrics, judgments, experiments, monitoring, diagnostics, and acceptance thresholds for relevance feedback.

Implementation guidance

• Build the smallest deterministic fixture set that spans risk classes, then add production-derived cases only after privacy-safe review.
• Model Relevance feedback inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable Relevance feedback contract or policy version with material state so migrations, replay, and support decisions remain attributable.
• Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.

Failure handling and safeguards

• Invalid filters or unavailable required indexes return a clear bounded failure; optional enrichments may degrade only when declared non-authoritative.
• Invalid or contradictory Relevance feedback state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
• Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.

Verification and operations

• Run the suite twice from a clean state and confirm that ordering, retries, and parallel execution do not change the verdict.
• Monitor Relevance feedback success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
• Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.

Adoption assumptions

• Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
• The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.

The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.