Finance & Analysis

Verified against ChatGPT · 2026-08-13

Build an M&A diligence question list organized by risk category instead of a generic due-diligence template

Generates a due-diligence question list tailored to the specific deal and target, sorted by which risk category each question actually probes, so the questions asked reflect what could actually break this deal rather than a boilerplate checklist.

ChatGPT (GPT-5.1)4 fillable variables

The prompt

Ready to copy — highlighted parts are example details you can swap.

I'm preparing diligence questions ahead of an M&A conversation and I don't want a generic due-diligence checklist pulled from a template — I want questions organized around the specific risk categories that matter for this deal, given what I already know and don't know about the target.

DEAL TYPE AND STAGE
Acquiring a competitor's book of business, early-stage conversations before an LOI

TARGET COMPANY OVERVIEW
~$4M ARR, 40 customers, founder-led, 12 employees, no external funding raised

WHAT WE ALREADY KNOW
Top 3 customers make up roughly 35% of revenue based on what the founder mentioned informally; no known litigation

SPECIFIC CONCERNS GOING IN
Customer concentration risk, whether key engineering staff would actually stay post-acquisition, and whether the founder-led sales process would transfer to our sales team

Organize the diligence questions into risk categories relevant to this specific deal — likely including financial (revenue quality, customer concentration, working capital), legal/contractual (change-of-control clauses, litigation exposure), operational (key-person dependency, systems and process maturity), and any category suggested by the specific concerns I named — do not just default to a generic four-category template if the concerns I listed point to something else that deserves its own category (e.g., a specific regulatory exposure). For each category, generate questions that probe what we don't already know rather than re-asking what's in the target overview I gave you — a diligence list that re-derives publicly known facts wastes the limited time available with the target's team. For the specific concerns I named, make sure at least one pointed, hard-to-deflect question directly addresses each one — not a soft version of the question that could be answered evasively without actually resolving the concern.

WHAT NOT TO DO
Do not invent a specific red flag about this target that I haven't given you evidence for — frame concerns as things to verify, not as findings, since you have no actual access to the target's real financials or legal history. Do not pad the list with generic questions that would apply to any company being acquired if they don't connect to what's actually known or unknown about this specific target.

OUTPUT FORMAT
Sections by risk category, each with 3-6 pointed questions. A short "highest priority" subsection at the top pulling out the 5 questions across all categories most directly tied to the specific concerns named. Close with one line noting this is a structuring aid to prepare for diligence conversations and does not substitute for qualified legal, accounting, or M&A advisory review of the actual target materials.

Customize

Optional — swap in your own details for the highlighted parts above.

Why this works

Instructing the model to build questions that probe what's unknown rather than re-deriving what's already in the target overview directly addresses the biggest practical waste in real diligence conversations: target management teams' time is limited and closely guarded, and a generic checklist that re-asks basic facts already disclosed burns that scarce time on questions that don't move the deal forward — anchoring the question generation to the known/unknown split given in the prompt keeps every question earning its place in the conversation. Requiring at least one pointed, hard-to-deflect question per named concern — rather than a softer, generically phrased version — matters because a vague question ("how do you think about customer concentration?") invites a reassuring, non-committal answer, while a specific one ("if your top customer left in the next twelve months, what would happen to the other 65% of revenue's growth trajectory") is much harder to answer evasively, and evasiveness itself is diagnostic information the acquirer needs to notice. The explicit instruction not to invent a specific red flag about the target is an important guard against a real risk with this kind of prompt: a language model generating a diligence list can slip from asking a probing question into stating a plausible-sounding concern as if it were an established fact about this particular target, and presenting a fabricated finding with the same confidence as a real one could misdirect the acquirer's actual diligence effort or even surface in a written memo as if it had evidentiary basis. The mandatory closing scope note — that this organizes preparation and doesn't substitute for qualified legal, accounting, or M&A advisory review — keeps the output correctly framed as a conversation-prep tool rather than something that could be mistaken for the actual diligence work itself, which requires real document review by qualified professionals.

What you get back

Highest priority: "Walk us through what happens to the top-3-customer relationships if there's a change of ownership — are any of those contracts personally tied to the founder's relationship rather than the product itself?" Financial category: "Beyond the informally cited 35% concentration figure, can we see actual customer-level revenue for the trailing 12 months to verify that split precisely?"

Verified against

ChatGPT GPT-5.1 · 2026-08-13

Changelog

  • 2026-08-13 Initial publish, verified against ChatGPT GPT-5.1.

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