Chunking: Requirements and source contract

Defines inputs, outputs, quality expectations, source rules, access constraints, and failure behavior for chunking. This reference fixes the adoption boundary, failure behavior, and observable evidence while leaving 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 Chunking, adopt a versioned source, transformation, index, and access-control lineage. Make the user-visible promise, exclusions, authority, and failure response explicit while leaving measured thresholds as adoption choices. 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 Chunking actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this requirements 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

• Preserve source identifier, version, location, access scope, parser, normalization, chunk, embedding, and index versions for every retrievable unit.
• Apply access filters before results, snippets, counts, caches, and model context are exposed; post-filtering alone is not sufficient.
• Quarantine parsing or transformation failures and prevent a partial artifact from silently replacing the last complete version.
• Evaluate retrieval and grounded-answer behavior on versioned representative cases, including conflicts, absence, staleness, and adversarial text.
• State which actor receives which outcome under each material state, including unavailable, unauthorized, empty, and partial conditions.
• Mark configurable limits and policies as choices with owners; do not present sample values as universal requirements.
• Chunk on document structure with bounded overlap and preserve source offsets, headings, parent identity, access scope, and chunker version.
• Evaluation chooses size and overlap by retrieval and grounded-answer evidence; token count alone is not a quality metric.

Implementation guidance

• Review the contract with product, implementation, operations, security, and affected users before promoting it beyond review.
• Model Chunking inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable Chunking 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

• If provenance, access scope, or required content is missing, exclude the unit and expose a diagnosable coverage gap instead of fabricating completeness.
• Invalid or contradictory Chunking 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

• Map each promise and exclusion to at least one observable scenario and confirm that no two states require contradictory outcomes.
• Monitor Chunking 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.

References

• NIST AI 600-1, Generative AI Profile (applies as of 2026-09-08)