TL;DR: A five-section prompt forces the model to separate what it actually found from what it is guessing, so a smooth-sounding outreach draft can no longer hide weak research behind good writing.
Account research prompts usually mix confirmed facts, reasonable guesses, and sales copy into one continuous paragraph, and a reader has no easy way to tell which is which. This Reddit user, who works on Komo, an AI revenue agent, ran into that exact failure mode constantly and built a prompt pattern that fixes it by force.
The fix does not ask the model to write better. It asks the model to organize its output into five separate boxes, each with one job, so nothing sneaks through wearing the costume of a fact. That distinction matters more than it sounds. A sales rep skimming a research brief at 8am before a call block is not going to catch a subtle hedge buried mid-sentence. They need the uncertainty flagged where they cannot miss it, which is exactly what forcing everything into labeled buckets accomplishes.
Breakdown
Confirmed facts come first, and every single claim needs a source attached to it. If a source does not actually support a claim, that claim gets dropped instead of softened into something vaguer. This is the section doing the heaviest lifting, because it is also where most AI research quietly fails today. A model will happily write “the company recently expanded its engineering team” based on one LinkedIn post about a single new hire, and without a source citation right next to it, that sentence reads exactly as confidently as something actually verified.
Possible relevance is next, and it has to be labeled clearly as inference, never fact. The model also has to explain which confirmed fact the inference came from, so a reader can trace the logic instead of just trusting it. This is the section that lets a rep decide for themselves whether the leap makes sense, rather than inheriting the model’s confidence level as their own.
Unknowns is a running list of what still needs a human to check before anyone acts on it. This section alone kills a lot of false confidence, because most research prompts never admit what they do not know. Things like “unclear whether this team uses a competing tool already” or “no confirmation this contact still holds this title” belong here, and having them written down in plain sight changes how a rep approaches the call.
Recommended next step comes before any pitch. The model has to suggest more research or a clarifying question first, which slows down the rush straight to outreach. In practice this might mean flagging that a quick company-news search would confirm a funding rumor, or that the actual decision maker still needs to be identified before a message goes out at all.
Draft message is last, and it only gets written if the claim behind it is already supported in the sections above. The model is explicitly told not to invent personalization, which is the exact move that makes AI outreach feel fake once a prospect notices it. Prospects notice a lot more often than most sales teams assume, and one fabricated detail is usually enough to burn the whole message.
Why It Works
This works because it breaks the model’s default instinct to write one persuasive, continuous narrative. Left alone, a language model optimizes for a draft that reads smoothly, and smooth writing hides the seams between “the company raised a Series B last quarter” and “this company probably cares about scaling engineering fast.” Forcing facts and inference into separate labeled sections makes the model show its work at every step, the same way you would want a junior analyst to flag “I am assuming this, not confirming it” before it goes into a deck.
The original poster said it best: a fluent draft is not evidence that the underlying research is good. Fluency and accuracy are two different qualities, and most research prompts only ever optimize for the first one. A model under time pressure to sound confident will always choose confidence over accuracy unless the structure of the prompt takes that choice away from it.
One commenter, Admirable-Future-633, added a solid variation worth stealing: bolt on an explicit “do not know yet” section so the model cannot quietly pad out a thin signal into something that sounds like a finding. A second variation worth testing is capping the possible relevance section at three items, so the model cannot bury one real inference under a pile of speculative ones.
Use Cases 🎯
- B2B sales research before a cold outreach sequence goes out
- Account-based marketing briefs where reps need to trust the source, not just the copy
- Any AI research agent that hands its output to a human for a final judgment call
- QA on existing AI-generated outreach, by running the draft backward through the same five buckets to see what actually holds up
Prompt of the Day
Copy this straight from the original post:
Research this account using only the supplied sources.
Return five sections:
1. Confirmed facts
– Each fact must include its source.
– If a source does not support a claim, omit it.2. Possible relevance
– Clearly label this as inference, not fact.
– Explain which confirmed fact led to the inference.3. Unknowns
– List what would need human verification.4. Recommended next step
– Suggest research or a question before suggesting outreach.5. Draft message
– Only write one if the proposed claim is supported above.
– Do not invent personalization.
If your outreach prompts keep turning thin signals into confident-sounding messages, steal this structure and run it on your next account list. Head over to r/PromptEngineering for the full thread, the comments have a few more constraint ideas worth testing on your own research pipeline.
Frequently Asked Questions
Q: Why separate facts from inference instead of just writing a better prompt?
Smooth, confident-sounding narratives hide weak research. By bucketing facts, guesses, and sales copy separately, reviewers immediately see where evidence is thin. A commenter said this “upgrade” prevents the model from gap-filling and gives you a “cleaner next step” to review.
Q: What goes in the “do not know yet” section?
List anything that would need human verification. Gaps the model couldn’t confirm from your sources should go here. One commenter highlighted that explicitly calling out unknowns “stops the model from filling gaps just to make the answer feel complete” and gives reviewers a clear action before reaching out.
Q: Won’t this structure make drafts less persuasive?
Yeah, output will be choppier than a single smooth narrative. But that’s the point: a structured breakdown backed by sources is way more trustworthy for outreach than a polished pitch that blends guesses in. Reviewers can spot weak spots and decide whether to reach out.
Q: How do “Unknowns” and “Recommended next step” differ?
“Unknowns” flags gaps (e.g., “Don’t know their tech stack”). “Recommended next step” tells you how to close them (e.g., “Check their job posts or ask in conversation”). One moves from “what’s missing” to “what do we do now.”
Q: Does this pattern work beyond B2B outreach?
Absolutely. Any research where accuracy matters, including competitive analysis, customer support triage, and product decisions, benefits from separating facts, inference, and unknowns. The pattern forces clarity over prose, which makes better decisions across the board.
A prompt pattern that made account research less generic: separate facts, inference, and the next action
by u/Harshit-24 in PromptEngineering