WorkBuddy for case search ledgers: lock fields before trusting semantic anchoring

Today I came across an article about AI legal research. It claimed that a top-tier Chinese law firm increased accuracy from 61% to 92.4% using a "Three-Stage Semantic Anchoring Method." What I want to discuss here is whether WorkBuddy can turn similar case retrieval into a verifiable evidence chain. I've been using WorkBuddy for about a month, and I just started using the Workspace feature a few days ago; I'm currently testing it. Based on my personal experience, configuring source, permissions, and desensitization clearly is more urgent than simply pursuing semantic matching. Previously, I tended to focus on "whether it understands the case facts." Later, I realized that when colleagues receive the ledger, they need to directly verify which document, which page, who has viewing rights, and which information has been desensitized for each conclusion.

The ledger must be acceptable for verification. My work is typical: supplier framework agreements, similar case judgments, search materials in project emails, reimbursement screenshots—all eventually go into one ledger. Material sources are complex, and formats are inconsistent. I've used the Email Assistant for about a month and have a habit of categorizing email materials into project directories first. I've also used the Document Comparison feature for about a month; it solves "where do two documents differ," but not "can one ledger be delivered." I've used Semantic Comparison for about a month; it helps judge similarity but doesn't take on review responsibility for me.

The real trouble lies in the lack of traceable boundaries in what the model writes. I just started using Contract Summary a few days ago. When judgments, framework agreements, and email materials are mixed together, if the output lacks source anchors, lawyer review costs skyrocket. Once the model hallucinates facts, lawyers spend more time verifying hallucinations than the search itself.

The solution is to use WorkBuddy to build a verifiable evidence chain ledger. I'll try to write the operation path for first-time users. Interface names may vary slightly by version, but entry points are generally similar. My approach this time was to lock down the ledger columns first, then discuss semantic anchoring.

Open WorkBuddy, click "Workspace" on the left, click "New" in the top right corner, select "Document Table," and name it "Similar Case Search Evidence Chain." I just started using the Workspace a few days ago; essentially, it's a place to put project files, tables, and permissions together. The expectation at this step is to see a blank table and a "Materials" panel on the right.

Click "Add Materials" and drag PDFs, Words, project email materials, and screenshots in together. Select "Extract" for the task type initially, not "Generate." This means exactly what it sounds like: let it extract verifiable information first, don't let it casually write legal opinions. I just started using generative capabilities like Contract Summary a few days ago, so I'm temporarily keeping them out of the main workflow.

Above the table, click "Column Configuration" and add columns one by one: Case Name, Opposing Party, Cause of Action, Fact Anchor, Legal Application Anchor, Judgment Result Anchor, Source File, Page Number, Review Status, Desensitized Yes/No. Fact Anchors correspond to "Upon trial, it was found" or basic contract facts. Legal Application Anchors correspond to "This court believes" or clause bases. Judgment Result Anchors correspond to "Judgment is as follows" or concluding clauses. I treat this as an evidence chain template; every anchor must be viewable by colleagues via the source file.

Click "Validation Rules," set "Source File" and "Page Number" as required, set "Review Status" to "Pending Review / Reviewed," and set "Cause of Action" as an enumeration. Enumeration means fixed dropdown options, e.g., Contract Dispute, Labor Dispute, Intellectual Property. This prevents entries in the ledger that look like conclusions but cannot be verified by anyone.

Click "Permissions," create three tiers: I edit, Team read-only, Interns can only view the desensitized export table. Put raw materials in restricted directories; WorkBuddy outputs only reference page numbers and do not display full party information. Enable "Sensitive Column Masking," replacing names, Unified Social Credit Codes, and amounts with [Subject A], [Amount Range] first. Permissions ensure that when the ledger flows between roles, it's clear who can see what and which materials are desensitized.

The key to my configuration is turning the "Three-Stage Semantic Anchoring" from the article into executable delivery rules in WorkBuddy: extract facts first, then bases, finally results. Simultaneously, every result must carry source, page number, and review status. In my environment, when WorkBuddy handles multi-material organization, results are much more stable as long as input/output rules are clear.

The ledger is finally verifiable. Over the last two days, I tested it with a batch of supplier framework agreement materials and similar case materials. After about forty minutes, upon exporting CSV, the source column was mostly filled, and page numbers were traceable. Previously, the most annoying thing was colleagues asking me "Where did this come from?" after receiving the ledger. Now, because source anchors are clear, they can at least directly open the corresponding file to check. If opposing lawyers use search results without source page numbers as arguments, I'd probably write two extra paragraphs in the cross-examination opinion.

I've also set three rules for daily maintenance: spot-check 5 items before leaving work each day, update enumeration options weekly, and tag new materials before adding them to the database. Pitfall avoidance advice is just one sentence: Don't let WorkBuddy give conclusions directly; let it provide a verifiable evidence chain first, then lawyers make the judgment.

Applied to office tools, Three-Stage Semantic Anchoring can be understood as fixing the evidence chain columns first, then letting the model extract.

WorkBuddy building legal search ledgers: Accuracy comes from running through source anchors, permission boundaries, and verifiable workflows.