Define a bounded review task
Choose the document type, intended users and decision the system supports. Extracting termination dates, comparing a contract with an approved playbook and drafting a research summary are separate tasks. Specify the output fields and what a reviewer must approve before an output is used.
Keep extraction separate from interpretation. A system can locate a liability clause without determining whether its terms are appropriate for a particular transaction. Record the applicable playbook version and escalation owner. Treat a missing or ambiguous result as a review item rather than silently accepting a default.
Preserve document provenance and source spans
Assign each document a matter, identifier and version. Preserve the original file alongside extracted text and page references. Check OCR quality on scanned pages, tables, signatures and amendments. A correct model applied to incomplete text can still produce an incorrect result.
Return the clause text, page or section reference and relevant cross-references with each extracted field. Defined terms, schedules and later amendments can change the meaning of a passage. Give the reviewer access to that context and record which version was reviewed. Prevent an obsolete draft from being mistaken for the executed document.
Evaluate extraction and review outcomes separately
Create a representative set with reviewer-approved reference labels and a written labeling policy. Include absent clauses, alternative wording, scans and amended agreements. Keep documents from the same template family or transaction from leaking across development and final evaluation when that would overstate generalization.
In a hypothetical extraction test, a system proposes 50 fields, of which 40 are correct, while the reference contains 80 required fields. Precision is 40 divided by 50, or 80%; recall is 40 divided by 80, or 50%. Neither number measures whether the contract is acceptable or establishes a TensorBlue client result.
Report errors by field and document condition, including wrong dates, missed exceptions and unsupported answers. Measure correction time and reviewer acceptance separately from model scores. Compare the assisted workflow with the existing review process using the same task and quality criteria. Do not claim savings from inference speed alone.
Verify research citations against actual sources
A generated case name, citation or quotation is a candidate to verify. Retrieve the underlying source, confirm that it exists and check that the cited passage supports the statement. Record jurisdiction, date and source version so a reviewer can assess whether it belongs in the requested research scope.
Source existence does not establish that an authority remains applicable. Assign review of current status and relevance to the responsible legal professional using appropriate research resources. When a source cannot be verified, expose that limitation in the draft instead of filling the gap with a plausible citation. Preserve the search scope and evidence used for the memo.
Control matter access and professional review
Enforce matter-level authorization in retrieval and document storage, including exports and diagnostic logs. Review provider data handling, retention and training settings before processing client material. Test access with users from different matters and verify deletion across retained copies. A private deployment alone does not prove these controls work.
The ABA announcement of Formal Opinion 512 discusses US model-rule considerations for lawyers using generative AI, including competence, confidentiality, consent and fees. It provides a jurisdiction-specific reference, not universal approval of an implementation. Have the responsible professional determine the obligations that apply to the actual matter and workflow.
Record reviewer corrections and final approval. A generated redline or memo should not automatically become an accepted change, client deliverable or filing. Define who can approve each output and preserve a trace from the final work product to its reviewed sources.
Operate and budget the complete workflow
Version the extraction schema, prompts, model configuration, playbooks and document-processing pipeline. Re-evaluate changes on the held-out set before rollout. Monitor failed ingestion, missing source spans, unverified citations and review queues. Provide a manual fallback when a service or evidence source is unavailable.
Estimate cost from actual document volume, page quality, OCR, retrieval, inference, integration, storage and reviewer time. Separate measured pilot results from projections. Use the MLOps release guide, explore AI workflow scoping or discuss a bounded document-review pilot.