Breaking Down the $770 Billion News into a Trackable Spreadsheet

When seeing news like "The $770 billion giant is going to buy the dip," don't ask what to buy first; break the story down into a verifiable table. I just started using Excel yesterday and RAG three days ago, but while building an AI industry monitoring solution for a client, I happened to set up this method. Technical feasibility is fine; the difficulty lies in not treating emotion as data.

On Day 1, build the skeleton first; make no judgments. Open Excel, create a new blank workbook, and name the file "AI Industry Chain Observation." If using the web version, click New in the top left. Select A1 to H1 and fill in the headers: Company, Keywords, Event Source, Original Statement, Verifiable Metrics, Impact Judgment, Evidence Status, Update Time. Keywords are terms you can search later to locate it; Original Statement is exactly how it's written in the news—don't edit it yourself; Verifiable Metrics are things you can cross-check against financial reports, announcements, or industry data. Enter "Apple" in A2, "Tariffs, iPhone, Service Revenue" in B2, "ChinaVenture/Huxiu" in C2, and "Market cap loss of $770 billion" in D2. Enter "Quarterly profit, China region sales, Service revenue" in E2. Leave F2 empty for now. Enter "To be verified" in G2 and "2026-09-06" in H2. Common phrasing in news is "going to buy the dip"; treat this only as an opinion, and judge after verifying metrics. This way, there's at least one traceable row in the table. If the client asks, you know where the statement came from and what to check. Beginners often copy headlines directly into "Original Statement" and casually write "Positive" in "Impact Judgment." Don't write that yet; save judgment for later.

On Day 3, supplement evidence and filter. Go back to the table, select Row 1, click Data > Filter. You'll see small arrows next to each header. Click the dropdown for Evidence Status, check only "To be verified," and expect to see a bunch of incomplete cells. For the Apple row, update D2 to a verifiable version: quarterly profit of $24.78 billion, up 4.8% YoY, with China being the only region where Q3 sales didn't grow. Change E2 to "Quarterly profit, YoY growth, China region revenue, Service revenue." If multiple news items report the same event, don't add duplicate rows. Add a header "Event ID" in column I, using the same ID for the same event (e.g., Apple tariffs and market cap fluctuations can both be labeled "A-001"). Select G2 to G100, click Home > Conditional Formatting, choose Highlight Cells Rules, then Equal To, enter "To be verified," and select light yellow. Expect to see yellow alerts. These past few days, I've also been trying RAG, throwing news into a retrieval pool for summarization. But there's a trap: if negative corpus mixes in, the model might continue writing it as fact. Adding "Evidence Status" to the table is precisely to prevent the model from turning guesses into conclusions for you.

After a week, consolidate onto one page. Create a new worksheet named "One Page." Go back to "AI Industry Chain Observation," select the headers and data range, click Insert, then PivotTable. If prompted to choose a location, select a new worksheet in the current workbook. Drag "Company" to Rows, "Evidence Status" to Columns, and "Company" to Values (set to Count). Expect to see rows for Apple, columns for To be verified/Verified, and counts in the cells. Create another column "Business Impact," using short phrases for costs, orders, compliance, and customer willingness to pay. For example, the Apple row can note tariff disruption to supply chain, service revenue providing a floor, and China region sales needing separate tracking. When outputting, discuss only three things: what the event is, whether metrics can be verified, and what's missing for judgment. Don't talk about "buying the dip." Client willingness to pay usually comes from your ability to break risks into trackable fields, which is more useful than guessing boldly.

Date formats get messy; mixing "September 6" and "2026-09-06" breaks sorting. Standardize input as 2026-09-06 and set cell formatting. Stock price volatility easily pollutes fundamentals; market cap evaporation doesn't equal profit decline, and earnings growth doesn't equal stock safety, so Original Statements and Verifiable Metrics must be separated. Duplicate news creates false heat; the same sentence reposted by three outlets looks like three events. Use Event IDs to deduplicate. Don't ignore permissions; if the table contains client lists or undisclosed financial metrics, desensitize first. When making solutions, I habitually start with local tables, confirming source types before considering API integration.

Once you've run the table for a week, consider automated collection. You can find friends to script news title scraping, or integrate cleaned records into a retrieval Q&A system. Don't reverse the order: manually prove it works first, then talk about engineering. Whether buying the dip holds depends first on whether the story can be broken down into verifiable fields.