AI for research: the four moves.

Research is a use case where AI assistants make dramatic-looking demos and mostly fail at the actual daily work. This post is what actually works, from watching researchers use LADLE.

**Move one: the primary-source triage.**

You have a claim you need to verify. Someone said a study showed X; a headline claimed Y; a talk mentioned Z. Instead of searching for confirmatory secondary coverage, ask LADLE (with search on):

"Someone claimed [X]. Find the primary source — the actual study, report, or dataset. Give me the URL and the specific page or section number where the claim is supported. If you can't find one, tell me."

The reply either gives you the primary source (good — you go read it) or admits the primary source is hard to locate (also good — that's information; either the claim is a game-of-telephone artifact or it comes from something not publicly available, and you now know which).

This move alone saves researchers an hour a week that used to go to searching news coverage of the claim and getting distracted by adjacent stories.

**Move two: the "what would change my mind" prompt.**

You've formed a hypothesis. Before you commit to writing it up, ask:

"I believe [hypothesis]. What are the strongest counterarguments? What evidence would, if true, most substantially undermine this? What are researchers who disagree usually pointing to?"

The reply is a stress test. Not a rewrite, not a confirmation, not a validation — an argument against. Real researchers use this before defending a claim they care about, because the model doesn't have their ego attached to the conclusion.

The best researchers we've watched use this treat the reply as a checklist: for each objection, they either address it in the write-up or note why they're setting it aside. That's the move.

**Move three: the structured extraction.**

You have a set of documents (papers, reports, filings) and you need to extract the same information from each. Instead of reading them all in full and taking notes:

Give LADLE the schema first: "For each document, I need: author list, publication date, primary claim, sample size, key methodology, and one thing that seems weak in the design."

Then feed documents one at a time (or in a batch if they fit context). The reply is a structured extraction per document. You verify the extractions against the actual documents for the ones that matter (samples the highest-stakes ones); the rest you trust with light spot-checking.

This works when the extraction schema is well-defined and the documents share genre. It doesn't work well for exploratory reading where you don't know what you're looking for yet.

**Move four: the cited brief.**

You've done the research. Now you need to write it up for someone who won't read your full notes. Ask LADLE:

"Turn my notes into a 300-word brief for [audience]. Use only claims from my notes. Cite specific documents by the identifiers I've used. If there's a claim in the notes without a citation, flag it."

The reply is a brief that mirrors your notes' claims, not the model's own knowledge. The flag on uncited claims is the important part — it catches the places where you know something but haven't sourced it in your notes.

This is the one that turns raw research into shareable output faster than any other move.

**What NOT to do:**

Don't ask LADLE to "do the research for you." The model doesn't do research; it retrieves and synthesizes. Research is about deciding what to look for, judging what you find, and reconciling conflicts. Those are the human parts.

Don't trust unverified claims from the reply. LADLE hallucinates less than earlier models, but "less" isn't zero, especially on specific figures, dates, and citations. Verify anything you'd stake a reputation on.

Don't use LADLE as your citation manager. It's a bad one. Zotero, Obsidian, or a real reference manager is the tool for that.

**Rule of thumb.**

LADLE is useful in research as a structured thinking tool with retrieval. It's not a substitute for the reading and the judgment. Used well, it saves you the mechanical work of triage, extraction, and drafting — not the intellectual work of research itself.