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Dave Wang · Creator

4 AI moves that turn Claude from a chatbot into a research analyst

Instagram post adapted from a SPECIFIC captured source post — exact-source traceability, like our YouTube flow.

Instagram carousel
Validated — exact source linked@nicolasboucherfinance
https://www.instagram.com/p/DUwAfD7EhPt/

Observed format: Carousel: cover hook + 4 numbered "power moves" slides + comment-keyword CTA slide with a bonus masterclass offer.

Observed hook: "Claude is basically a finance analyst that never sleeps. Most teams use it like a chatbot but the real leverage comes when you make it build inside your workflow."

Adaptation: Kept the structure (cover hook + 4 numbered moves + comment-keyword CTA) and the "most people use it like a chatbot, real leverage is building it into the workflow" spine. Changed every word and the topic: source pushed Excel modeling, voice-to-text, and live dashboards for finance teams; WSP version reframes the 4 moves around analyst primary-source work — reading 10-Ks, earnings guidance-vs-actual, definition-based comps, and bear-case stress-testing. Swapped keyword "Claude" to "ANALYST", replaced the masterclass-funnel bonus with a prompts + checklist asset, and added an explicit educational-only / no-recommendation disclaimer slide that the source did not have. No claims, visuals, or phrasing copied.

Cover

Cover slide

Most analysts use AI like a search bar. The edge is making it work the way a real analyst does.

Slides

One idea per slide

Slide 1
4 AI moves that turn Claude from a chatbot into a research analyst. Most analysts use AI like a search bar. The edge is making it work on the documents you already verify.
Slide 2
MOVE 1 — Read the filing, not your memory of it. Feed it the actual 10-K. Ask for the risk factors that changed vs last year, quoted word for word. You check it against the source.
Slide 3
MOVE 2 — Guidance vs actual, in one table. Drop in the earnings call transcript. What management promised last quarter, what they delivered, where the language got softer.
Slide 4
MOVE 3 — Comps by definition, not vibes. Same multiple, same period, same accounting basis. Build the screen on rules so the spread you see is real, not an artifact.
Slide 5
MOVE 4 — Argue against your own model. Have it build the bear case on your assumptions. Surface the weak inputs before the market does.
Slide 6
This does not replace your judgment. It removes the grunt work between you and the evidence. Educational only. No buy, sell, or hold. Verify everything against the primary source.
Slide 7
Want the exact prompts plus the workflow checklist behind these 4 moves? Comment ANALYST and we will send it over.

Caption

Paste under the post

Most people treat AI like a smarter search bar. Ask a question, copy the answer, move on.

That is the slowest way to use it.

The leverage shows up when you point it at the same primary sources an analyst already reads: the 10-K, the earnings call, the comp set, the model you already built. Not "what do you think of this stock." Structured work on documents you can verify line by line.

4 moves we use inside a real research workflow:

1. Feed it the actual 10-K, not your memory of it. Ask it to pull the risk factors that changed versus last year and quote the exact language. You verify against the filing.

2. Turn an earnings call transcript into a guidance-vs-actual table. What management promised last quarter, what they delivered, where the wording got softer.

3. Build a comp screen by definition, not vibes. Same multiple, same period, same accounting basis, so you are comparing like for like before you trust the spread.

4. Stress-test your own assumptions. Have it argue the bear case against your model so the weak inputs surface before the market finds them.

None of this replaces your judgment. It removes the grunt work between you and the evidence.

Educational only. No buy, sell, or hold. No targets, no returns. Verify everything against the primary source.

Want the exact prompts plus the workflow checklist behind these 4 moves?

Comment ANALYST and we will send it over.

Layout

7-slide carousel, 1080x1350. Slide 1 is the cover: big bold headline top-aligned, supporting line below, small WSP wordmark bottom-left, subtle swipe arrow bottom-right. Slides 2-5 share one template: a small green "MOVE n" eyebrow tag top-left, a short bold title line, then 2-3 lines of body in lighter weight, generous negative space. Slide 6 is the disclaimer/positioning slide, same template minus the move tag, with the legal line in smaller muted text at the bottom. Slide 7 is the CTA slide: centered, the keyword "ANALYST" rendered large in green inside a rounded pill, instruction line above and below.

Design notes

WSP palette: near-black background (#0B0E0C / off-black), primary text in off-white (#F2F4F1), accent and keyword highlight in WSP green (#1FdF6A / signal green), muted secondary text in cool gray. Typography: Montserrat throughout — ExtraBold for headlines and the MOVE tags, SemiBold for slide titles, Regular/Medium for body. Tight headline leading, comfortable body leading. Thin green hairline rule under the eyebrow tag on move slides. Keep one accent color only; no gradients, no stock photos, no emojis on-image. Consistent 80px side margins, slide number dots bottom-center on slides 2-7.

Why this works

The comment-keyword carousel works because each numbered slide is a self-contained, swipeable promise (high completion + save rate), and the keyword comment drives the engagement signal Instagram rewards while building a DM list. It fits WSP because the "stop using it like a chatbot, use it inside the workflow" framing maps cleanly onto our operational, anti-hype angle: concrete, verifiable tasks on 10-Ks, transcripts, and comps rather than vague AI hype.

CTA

Comment-keyword mechanic: "Comment ANALYST and we will send it over." Adapts the source's "Comment 'Claude'" DM-the-link play, swapping the keyword to ANALYST and the deliverable to a prompts + workflow-checklist asset instead of a cheatsheet/masterclass funnel.

Wall Street Prompt
Wall Street Prompt — internal