Most people type "is this stock good?" into AI and get a confident, useless answer.
The fix is not a secret code. There is no hidden "/godmode." What actually changes the output is giving the model a real analyst task and a real source to work from.
Here are 5 prompts we use against a single 10-K or earnings transcript:
1. Segment revenue: "Pull revenue by segment for the last 3 fiscal years and flag what changed." Forces it to work from the numbers, not vibes.
2. Risk delta: "List every risk factor that is new vs last year's 10-K." This is where the real story usually hides.
3. Call breakdown: "Summarize the earnings call into guidance, margins, and any question management avoided answering."
4. Comps: "Build a comparison table of this company against 3 named peers on the same 4 metrics." You name the peers and the metrics so it cannot drift.
5. Two-sided: "Write the bear case and the bull case, and cite the filing line for each claim." Kills one-sided confidence.
The rule underneath all 5: paste the actual filing or transcript and tell it to cite. AI without a source is guessing. AI grounded in a primary document is doing analysis you can check.
This is an educational research workflow, not financial advice. No buy, sell, or hold. Verify every number against the original filing before you trust it.
Comment ANALYST and we will send the full one-page prompt sheet (all 5, copy-paste ready).