You do not need to code to build an AI research workflow. The blocker is process, not Python. Source-backed extraction, a fixed schema, a comp set you can audit, and your judgment at the end. Human judges. AI builds. Educational only. Not investment advice.
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Wall Street Prompt · LinkedIn one-page
AI For Investors Who Are Not Coders
LinkedIn one-page
You do not need to code to build a source-backed research operating system. You need a workflow.
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Dense one-page content — sections, tables and frameworks
THE MISTAKE MOST NON-CODERS MAKE
The blocker is not Python. It is treating AI like a search box instead of a research system.
| Common move | Failure mode | Disciplined fix |
|---|---|---|
| Open-ended prompt | Confident, unverifiable answer | Constrain to a named document and a fixed schema |
| No source attached | Model invents figures from memory | Force every claim to cite the page or line |
| One-shot question | No audit trail to review later | Run a repeatable template you can re-run next quarter |
| AI gives the verdict | Outsourced judgment, hidden risk | AI extracts and structures. You judge. |
- They ask one open question, accept one answer, and act on it.
- They paste a 10-K into a chat and ask for a verdict, not a structured read.
- They confuse a fast answer with a sourced answer.
Why it matters: a non-coder with a disciplined workflow beats a coder with no process. The edge is the system, not the syntax.
THE NO-CODE RESEARCH OPERATING SYSTEM
Five layers. None require writing code. Each layer has one job and one output you can check.
| Layer | Job | No-code tool | Output you can audit |
|---|---|---|---|
| 1. Source | Hold primary documents | 10-K, 10-Q, earnings transcript PDFs | A named, dated file set |
| 2. Extract | Pull facts, not opinions | Chat with documents attached | Tables of figures with page cites |
| 3. Structure | Standardize the read | Spreadsheet or doc template | Same fields every company, every quarter |
| 4. Compare | Build the comp set | Side-by-side table | Peers on identical metrics |
| 5. Decide | Apply human judgment | IC memo template | Your thesis, risks, and what would break it |
Key: the model lives in layers 2-4. Layer 5 is always you. Human judges. AI builds.
THE EXTRACTION PROMPT FRAMEWORK
Stop writing essays to the model. Give it a fixed schema. A schema is just a list of fields you want filled.
| Field to request | Why you want it | Verify against |
|---|---|---|
| Revenue by segment | See where growth actually comes from | 10-K segment note |
| Gross and operating margin | Quality of the business model | Income statement |
| Cash from operations vs net income | Earnings quality check | Cash flow statement |
| Debt maturities and covenants | Solvency and refinancing risk | Debt footnote |
| Management's stated risks | What they admit can go wrong | Risk factors section |
- Attach the source first. Then ask only what the source can answer.
- Demand a citation column. No page reference, no entry.
- Mark anything not stated as 'Not disclosed' instead of letting the model guess.
Heads up: if the model fills a field the document never mentions, your prompt failed, not the company. Tighten the constraint.
BUILD THE COMP SET WITHOUT CODE
A comp set is one table where every peer is measured the same way. That is all 'a model' means at this stage.
| Step | Action | Discipline rule |
|---|---|---|
| 1 | Pick 4-6 true peers | Same business, not just same sector |
| 2 | Run the same extraction prompt on each | Identical fields, identical source type |
| 3 | Paste outputs into one table | One row per company, one metric per column |
| 4 | Flag the outliers | Ask why, do not assume which way is right |
| 5 | Write the read | State the question your comp set still cannot answer |
Why it matters: comparability is the whole point. If two companies define a metric differently, the comparison is noise until you reconcile it.
VERIFICATION: THE STEP NON-CODERS SKIP
Speed without verification is just faster errors. Build the check into the workflow, not after it.
| Risk | How it shows up | Control |
|---|---|---|
| Hallucinated figure | Clean number, no source | Citation column required, spot-check 3 |
| Stale data | Model uses old filing | Date-stamp every source file |
| Cherry-picked period | Only the good quarter shown | Force trailing 4-8 quarters |
| Anchored thesis | AI agrees with your prior | Ask it to argue the bear case separately |
- Trace three random figures back to the source document every session.
- If a number cannot be traced, treat the whole output as suspect.
- Re-run the same template next quarter. Drift in the answers tells you what changed.
Tip: position sizing and risk are judgment calls. The workflow informs them. It does not make them for you.
Caption
LinkedIn post copy
Visual design notes
- Near-black forest-green background. ONE teal-green accent only, used for the title keyword, the subtitle banner bar, and table header rows. Everything else off-white and muted gray.
- Heavy condensed sans headline at top (think industrial/grotesk), left-aligned. Title 'AI For Investors Who Are NOT Coders' with NOT in the green accent, all-caps, tight tracking.
- Subtitle sits inside a thin full-width teal banner bar directly under the title.
- Dense 2-3 column tables with thin 1px hairline dividers, alternating row shading at 4-6 percent opacity. Header row in accent green with dark text for contrast.
- Section headings in small all-caps green labels with a short rule line to the left, stacked vertically down the page, all left-aligned.
- Insert one simple 5-box vertical stack diagram for the 'Operating System' section: Source -> Extract -> Structure -> Compare -> Decide, with the last box (Decide/You) outlined in accent to signal human judgment.
- Footer pinned to bottom in small muted caps. Maintain generous left margin and consistent baseline grid so the density reads as engineered, not cramped. 1080x1350 portrait.
Production checklist
- ☐Design the 1080x1350 one-pager in the WSP template: near-black/forest bg, single green accent, condensed headline, teal subtitle banner.
- ☐Build all five section tables with consistent column widths, hairline dividers, and green header rows; verify text fits without overflow at export size.
- ☐Draw the 5-layer vertical OS diagram (Source to Decide) with the final 'Decide / You' box outlined in accent.
- ☐Add header title with 'NOT' emphasized, section labels with rule lines, and the WSP footer line at the bottom.
- ☐Proofread for voice compliance: short declarative sentences, no em dashes, no hype, no buy/sell/hold or targets, educational framing intact.
- ☐Export PNG at 1080x1350 for the LinkedIn feed and a PDF version for DM and lead-magnet delivery.
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Save this and run one extraction template on one company this week. Comment the field you would add to the schema, and repost if it helped a non-coder on your feed.