Preventing outdated news in research reports: built a review pipeline with WorkBuddy

Bottom line first: When AI-written research reports fail, what really needs fixing isn't the prompt, but strictly controlling source, date, and publication status. I've been using WorkBuddy for about a month, setting up a review pipeline for daily and weekly reports. Morning call prep time dropped from 90 minutes to 56 minutes, saving roughly 37.5%. These numbers are my estimates and may not be rigorous, but the process is definitely more stable.

Without strict review, AI directly "time-traveled" old news from two years ago to today.

Seeing this news recently, my first reaction wasn't that analysts are lazy. Templated production is too smooth: apply template, import data, cite public info, draft in minutes. The smoother it goes, the easier it is for humans to only proofread text, not sources. Especially young colleagues using AI tools often mistake things that "look like policy" for actual policy. My environment is foreign financial analysis; sell-side reports aren't my direct product, but internal morning meeting minutes, client briefs, and industry tracking are essentially the same: get one date wrong, and all subsequent judgments are skewed.

My approach is to change WorkBuddy from a writing tool to a quality control tool. The specific path isn't complex. Open WorkBuddy, click New Project, name it "Research Report Daily Review." Go to Data Sources, select Upload Files, and put in PDFs exported from Huibo Investment Research, internal Excel tracking sheets, and screenshots of policy originals. I just started using Huibo recently and haven't set up full auto-scraping yet; I'm only feeding it reports with confirmed sources to avoid bringing in polluted information. I spent a week building the knowledge base, including fixed field definitions, common company abbreviations, and policy term explanations. A knowledge base is basically giving the AI a searchable resource pack so it doesn't have to fish in the vast ocean of the public web.

The key is the template. I created a strict output matching template—basically forbidding free-form writing, only filling in fields I provide. Fields are: Event Date, Report Date, Source Name, Original Excerpt, Policy Entity, Credibility, Review Status, Publication Conclusion. At first glance, these fields seem verbose, but most research report blunders happen because event dates and report dates get mixed up. In the template, I enforce YYYY-MM-DD date format and limit original excerpts to 180 characters. After WorkBuddy outputs the table, I do a quick check on Source Name and Original Excerpt before deciding if it goes into the morning meeting.

There's a pitfall here. I wrote previously that WorkBuddy turned all dates into text when summarizing Excel. I later realized that with mixed data sources, don't expect a one-step solution. My breakdown is: extract first, then summarize, finally remind. During extraction, I only let WorkBuddy move fields; during summarization, I let it sort by industry, company, and date; during reminders, it only outputs "Pending Review" and "Low Credibility." If you throw a pile of PDFs and Excels at it to generate an analysis table, it mixes text, tables, and web summaries into a mess, and date columns are more likely to distort. The ROI of this tool depends not on how smart it is, but on how clearly you define the boundaries.

Permissions and collaboration must also be set. In my WorkBuddy project, I gave colleagues view-only access—they can see drafts and sources but cannot change status. Compliance or senior analysts get edit permissions to modify review status and publication conclusions. Final confirmation status can only be changed by me. The reason is simple: a half-baked Agent is better off delayed. Permission mishaps are more discouraging than slow generation speeds. Also, expired authorizations cause auto-sync failures. I hit this once; the knowledge base didn't update the next day, and after investigating for ages, I found the platform token had expired. A token is an authorization credential; when expired, the tool can't access resources. Now, every Friday, I manually check sync status, re-authorize, and see if any fields have drifted.

Daily maintenance is actually light. Every morning, I spot-check five items: Are dates today or within the last three days? Can the original excerpt be traced back to a specific page in the PDF? Is credibility marked as pending confirmation? On Fridays, I do a batch check, returning low-credibility, old news, and unclear policy entity entries to the source layer. When maintaining templates, I don't let AI add fields itself; new fields must be confirmed by me. Otherwise, today it adds a macro judgment, tomorrow an investment suggestion, and the day after, the review process becomes free-form writing.

This process doesn't guarantee beautiful research reports, but it at least stops old news from being treated as new policy. My judgment is that in the coming year, the competition among brokers and asset managers using AI won't be about who generates faster, but whose review chain is cleaner. Tools like WorkBuddy will shift from helping me write things to helping me block errors before publication. As for me, I'll keep wearing my suit and SpongeBob socks, as long as the field table stays organized.