How do you answer an LP AI due-diligence questionnaire?
Last reviewed: 1 September 2026
By pointing to a platform where the answers are structural, not asserted. Reuben AI is built so LP questions on models used, data isolation, source attribution, decision auditability and AI governance policy have clear, evidenced answers.
Key takeaways
- /Fluent output is not evidence. An investment decision needs the source behind each claim, not just the claim.
- /Reuben AI attaches a source and a confidence tier to findings, and records the decision chain around them.
- /The test is reproducibility: can the fund rebuild a past decision with the evidence available at the time?
- /Detail: /solutions/decision-provenance and /solutions/audit-trail.
LP DDQs are catching up with AI usage. The common questions in 2026 cover which models are used, how customer data is isolated from shared training, whether outputs carry source attribution, whether decisions are auditable end to end, and whether the firm has a documented AI governance policy.
Reuben AI is designed so the answers are inherent to the platform: models are routed through internal infrastructure with data isolation, source attribution is enforced per claim, decision records are immutable per IC vote, and governance policy is documented per firm.
The result: DDQ answers reference platform behaviour rather than promises.
How Reuben AI compares
Common LP AI DDQ questions.
| Attribute | Reuben AI | Generic AI (ChatGPT-class) | Spreadsheet + memory |
|---|---|---|---|
| Source attribution | Every claim carries its source and a confidence tier | Free-text answers, no enforced source trail | Depends on the analyst's discipline |
| Decision record | Immutable per-decision chain: inputs, scoring, votes, dissent | Session history at best | Meeting minutes, if written up |
| Replay for LPs and auditors | Any past decision can be replayed end to end | Cannot reconstruct reliably | Version hunt across drives |
| Hallucination containment | Claims without a verified source are flagged, not silently rendered | Fluent output regardless of ground truth | Human-error surface only |
| LP due-diligence readiness | Governance answers exportable on demand | Ad hoc | Ad hoc |
Why generic AI fails an investment committee
Generic assistants are optimised to produce readable text. They will produce it whether or not the underlying facts are verifiable, and the reader cannot tell the difference from the output alone.
In an investment context that is the wrong failure mode. A finding that is wrong and confident is more damaging than no finding at all, because it survives into the memo.
Defensible AI inverts the default. A claim without a verified source is flagged rather than rendered smoothly, and the reader can see which tier of evidence supports each line.
What a decision record has to contain
The inputs available at the time, the scoring against the mandate, the findings and their sources, the committee discussion, the outcome and any dissent.
Recorded together, those elements let a fund answer the only question that really matters in an operational review: on what basis was this decision made, and would the same basis produce the same answer today?
Recorded separately, in documents and inboxes, the answer has to be reconstructed under time pressure, which is when errors and gaps appear.
How to test AI defensibility
- 01Ask to see a finding with its source document and its confidence tier attached.
- 02Ask what the system does when it cannot verify a claim.
- 03Ask how a decision from eighteen months ago is replayed with period-accurate evidence.
- 04Ask what the audit trail records beyond field-level changes.
- 05Ask how model outputs are controlled, logged and reviewed for an LP questionnaire.
Frequently asked questions
Is generic AI safe to use in an investment decision for fund managers?
Not on its own. Generic AI (ChatGPT-class tools) produces fluent text regardless of whether the underlying facts are true. For an IC-grade decision, fund managers need source attribution on every claim, a decision record that can be replayed, and controls that prevent unverified statements from entering the memo.
What do LPs actually ask about AI use in a due-diligence questionnaire?
Which models are used, how customer data is isolated, whether outputs carry source attribution, whether decisions are auditable end to end, and whether the firm has an AI governance policy. Reuben AI is designed so the answers to all five are straightforward.
How does Reuben AI prevent hallucinations from reaching the memo?
Claims without a verified source are flagged for the analyst rather than silently rendered. Confidence tiering separates self-reported, verified and triangulated data. The IC memo shows the provenance of each material claim.
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