Bias is the silent killer of good decisions. Here’s a four-step prompt that forces AI to analyze both sides of any conflict, surface the hidden assumptions, and propose a solution that actually holds up.
Here’s the thing: AI is a mirror. You describe a conflict from your own angle and it validates your framing. You get confirmation, not analysis. That’s not a tool. That’s an echo chamber.
This happens because of how we naturally describe situations. We lead with our interpretation, not the facts. “My manager keeps micromanaging me” is already a verdict. The AI picks that up, runs with it, and hands you back a version of your own story with better vocabulary. You walk away feeling understood but no closer to a resolution.
A Reddit user in r/PromptEngineering spotted this and put together what they call the Logic Architect prompt. Short post, but the structure is genuinely useful.
How the Logic Architect Prompt Works
You give the AI a conflict. It works through four steps in order:
- Analyze the situation from Person A’s perspective
- Analyze it from Person B’s perspective
- Identify the unspoken assumptions both sides are making
- Propose a solution that satisfies the core needs of both
Step 3 is where the real work happens. Unspoken assumptions are where most conflicts actually live. Not in what people say out loud. In what both sides are taking for granted without ever naming it.
Think about a classic pricing disagreement with a client. Person A thinks the project is overpriced. Person B thinks it’s underpriced for the value delivered. On the surface it looks like a negotiation. But buried underneath, Person A assumes the deliverable is a commodity and Person B assumes the client understands the complexity involved. Neither has said that. The conflict isn’t really about price at all. It’s about two different mental models of what the work actually is.
By forcing a structural split before any synthesis, you stop the AI from picking a side and start using it as a neutral logic engine. Steps 1 and 2 create separation. Step 3 finds the actual fault line. Step 4 builds from there instead of splitting the difference and calling it a solution.
The order matters. If you asked for the solution first, you’d get something generic. Running the perspectives before the synthesis forces the model to earn its answer.
📋 Use Cases
- Team disagreements where both sides feel unheard
- Salary or contract negotiations before walking into the room
- Client conflict review before drafting a response
- Business decisions you’re emotionally attached to
- Any situation where you suspect you’re missing something obvious
- Co-founder or partner friction where the relationship makes it hard to think clearly
- Performance conversations you’ve been putting off because you’re not sure how to frame them
The business decisions category is underrated. When you’re close to something, you stop seeing it clearly. Running your own position through this as “Person A” and the skeptical investor, customer, or partner as “Person B” gives you a stress test you wouldn’t run on yourself.
Prompt of the Day
Here’s the exact prompt from the original post. Copy it directly:
[Describe a Situation/Conflict].
1. Analyze this from Person A’s perspective.
2. Analyze this from Person B’s perspective.
3. Identify the ‘unspoken assumptions’ both sides are making.
4. Propose a solution that satisfies the core needs of both.
Drop your situation at the top and let the structure do the rest. If you want sharper output, name the roles specifically: “Person A = my manager, Person B = my direct report.” The more concrete the framing, the more useful the result.
One variation worth trying: after step 4, add a step 5 asking the AI to identify which assumptions would be hardest to change. That tells you where the real friction is.
Another tweak that improves output quality: write the situation description in plain, factual language before you paste it in. No editorializing, no adjectives that imply fault. Just what happened and who said what. The cleaner the input, the less the model has to work around your framing to get to something neutral.
If the output from step 3 feels obvious or surface-level, push back. Ask the AI to go one layer deeper on each assumption. Nine times out of ten there’s a belief underneath the belief that’s doing the actual damage.
Try It This Week
Take the last disagreement you had at work or with a client and feed it in word for word.
Most people are surprised by what surfaces in step 3. What you were assuming without realizing it is usually more revealing than the conflict itself. The friction you thought was about the deliverable turns out to be about trust. The negotiation you thought was about budget turns out to be about control. That kind of clarity is hard to get from inside the situation, and impossible to get from an AI that just mirrors your input back at you.
This prompt works because it doesn’t let the model take shortcuts. It earns the answer. Head to the original thread in r/PromptEngineering to see what others are testing with it.
The ‘Code-Comment’ Priming Hack.
by u/Glass-War-2768 in PromptEngineering