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GUIDE / 6 MIN READ

Legal billing narratives: prepare a review queue without inventing work

By Alex Cinovoj, Founder & CTO, TechTide AI · CTO & Co-Founder, CPG Playbook

Published · May 2026 editorial collection

A time entry says “review correspondence.” Someone asks an AI tool to make it more descriptive. The tool adds a negotiation, a document analysis, and a client call that never appeared in the source record. The sentence reads better while becoming less reliable.

I would scope legal billing narrative assistance around finding entries that need a person's attention. The workflow can identify missing context, inconsistent formatting, or a possible mismatch with the firm's billing instructions. It should never invent work, increase time, or turn a vague entry into a factual account without confirmation from the person who performed the work.

This is a hypothetical administrative design, not legal or billing advice. The firm's authorized lawyers and billing owners must determine applicable requirements. The ABA's Formal Opinion 512 addresses reasonable fees and professional responsibilities in the use of generative AI; it does not make a generated narrative reliable by default.

Keep the original entry visible

The review screen should show the original narrative, recorded duration, matter identifier, author, date, and proposed issue. A suggested rewrite belongs beside the original, not in its place.

I would prevent the language component from changing recorded time or rates. If a billing owner needs to correct those fields, that should be a separate authorized action with its own reason and history. Combining narrative cleanup and financial edits makes review harder.

The output should also distinguish a formatting suggestion from a factual question. Expanding an approved abbreviation is different from asking what document was reviewed. The second requires information from the timekeeper or another authorized source, not a more assertive prompt.

Use the client's current instructions carefully

A firm may have client-specific billing requirements. The workflow should reference the approved, current version and identify the exact instruction behind a flag. It should not import rules from another client or treat an old guideline as current merely because it is easy to find.

For an illustrative matter, a guideline might require a task identifier. If the entry lacks one, the system can request the identifier. It should not select a plausible code based solely on the wording and finalize it without the firm's review process.

When instructions conflict or the applicable version is unclear, route the entry to the billing owner. The application should keep that exception visible instead of producing a confident compliance label. A model's assessment is a review aid, not a legal determination that the invoice is compliant.

Ask precise questions instead of filling gaps

A useful review question is “Which document did you review?” or “Does this entry describe one task or several?” A vague request to “improve the narrative” encourages unnecessary rewriting and may hide the real missing fact.

I would limit suggested wording to supported information. If the source says a contract was reviewed for defined issues, the draft can reorganize those facts. If the source only says “contract,” the draft should retain that uncertainty and ask for clarification.

The reviewer should be able to accept a formatting change, supply a missing fact, reject the suggestion, or leave the entry unchanged with a reason. Preserving those decisions helps the team identify recurring problems without assuming every model flag was correct.

Keep matter information within its access boundary

Billing narratives can reveal sensitive details even when they look administrative. I would minimize the material sent to the model and restrict the review queue by matter and role. The extraction or editing step should not search the entire document repository to enrich a short entry.

The workflow also needs a policy for logs. Capturing every prompt and response may duplicate confidential information into a less controlled system. OWASP's logging guidance is useful when deciding what operational evidence to retain and what sensitive content to exclude.

A practical record can retain the entry identifier, model or rule version, issue category, reviewer decision, and timestamps without placing full narratives in a broad monitoring channel. The firm's data owner should approve any more detailed retention needed for evaluation.

Separate review from invoice release

The first implementation should prepare a review queue and save approved narrative edits. It should not release an invoice merely because every item has a generated suggestion.

Invoice release may require additional checks, including financial reconciliation and client-specific review. Those existing steps should remain visible. If the narrative workflow finishes but the billing system write fails, the status should show that the approved edit is pending synchronization.

A repeated synchronization should update the intended entry once. Use stable entry identifiers and detect whether the destination changed after review. If a timekeeper edits the entry while the suggested revision is awaiting approval, refresh the comparison instead of overwriting the newer text.

Evaluate with known examples and known limits

I would create a synthetic test set containing terse entries, adequate entries, ambiguous abbreviations, missing task identifiers, and entries that mention several activities. Include a case where the model is tempted to add an unstated outcome such as “resolved” or “successfully negotiated.”

For each example, define whether the expected result is no change, formatting assistance, or a factual question. Have authorized reviewers judge the suggestions. Track unsupported additions separately from ordinary wording preferences because they have different consequences.

The evaluation should also include entries from different matter types where permitted. Success on one narrow billing style does not establish reliable behavior across the firm's entire practice. Keep the tested scope in the handoff documentation.

Measure review burden honestly

I would measure time spent reviewing flagged entries, the proportion of flags accepted, unsupported additions caught, and the number of edits reversed. Count the timekeeper's clarification effort as part of the workflow cost.

A system that produces more polished text but doubles review time may not be useful. A smaller rule-based check for missing identifiers could be the better first release. The implementation should be allowed to reach that conclusion.

The deliverable I would buy is a controlled queue with source-preserving edits, clear access rules, and an invoice-release boundary. It should make the billing owner's judgment easier to exercise while keeping the facts with the people who know what work occurred.

Demonstrate an entry that should stay incomplete

I would include a synthetic entry reading “review file” with a recorded duration and no other supporting detail. The acceptable output is a focused clarification request or an unchanged narrative awaiting the timekeeper. It should not name a document, claim a conclusion, or suggest that a call occurred.

Next, the timekeeper supplies a factual clarification. The workflow can propose a clearer sentence using that information, but the review record should preserve who supplied it. The recorded duration should remain unchanged unless a separately authorized correction is made.

I would then edit the original entry while its proposed rewrite is awaiting approval. The system should detect the revision conflict and prevent an older suggestion from overwriting the newer record. This demonstration gives the billing owner direct evidence of the two boundaries that matter: unsupported facts stay out, and approved edits apply to the record that was actually reviewed.

About the author

Alex Cinovoj

AI & Infra Engineer. Building AI that survives outside the demo.

Founder & CTO, TechTide AI · CTO & Co-Founder, CPG Playbook

I spent 13 years in US enterprise IT, the last 2 focused entirely on AI implementation. I run TechTide AI, an implementation studio that takes stalled AI pilots into production. I write about the work in progress at alexcinovoj.com and on Substack.

Logs over vibes. Production over theater.

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