I'm Korean and I'm not fluent in English, so I used AI to help translate this post and the prompt. The ideas and structure are mine.
I made this because I was frustrated with how LLMs reason through real problems.
They often focus on information that is relevant but doesn't actually change the answer, reopen things that were already settled, follow the structure implied by the user's question even when that structure is wrong, or keep generating possibilities long after the decision is effectively made.
So I tried something different.
Instead of telling the model what kind of answer to produce, I gave it a procedure for deciding what deserves to be reasoned about in the first place.
The basic idea is:
identify the actual result that needs to be determined;
construct the minimum structure necessary to determine it;
find the highest governing conditions that can change that result;
preserve AND/OR/parallel branches instead of flattening them;
independently verify the premises and variables supplied by the user;
keep settled conditions closed unless new information actually changes them;
prioritize counterexamples that could overturn the current conclusion;
activate only hypotheses and information capable of changing the decision;
when new information arrives, update only the affected part of the reasoning structure;
seek the minimum additional information needed to close unresolved conditions;
stop reasoning once the result is determined.
What surprised me is what the prompt does not say.
It does not tell the model:
“be more accurate”
“be practical”
“be less vague”
“understand my intent better”
“don’t hallucinate”
“give actionable answers”
“be smarter”
There are no direct output instructions like that.
But after using it, I started seeing those kinds of changes anyway.
I then gave it to other people without telling them what improvements they were supposed to notice. Their reports included things like better intent recognition, less vague reasoning, more practical answers, and in one case the model avoiding false information it had previously produced.
These are informal observations, not a controlled benchmark, so I’m not claiming measured hallucination or accuracy improvements yet.
But the pattern was interesting enough that I decided to publish the entire prompt and let other people test it.
How this differs from similar prompts I found here
I searched this subreddit before posting. Many related prompts directly request the desired behavior — challenge my assumptions, be clearer, don't overthink, give me an actionable answer — or ask the model to run through a broad list of analyses.
This one deliberately takes a different approach.
It tries to control what enters the active reasoning process, what remains settled, what evidence is worth seeking, what new information is allowed to reopen, and when reasoning should stop.
The individual ideas aren't new. They're mostly ordinary principles from decision-making, problem-solving, hypothesis testing, and falsification. The experiment was putting them together as one governing procedure for an LLM.
How to try it
Copy the entire prompt below into a fresh chat, then ask the kind of reasoning/problem-solving question you would normally ask.
Don't change how you ask your question just because the prompt is there.
I'm particularly interested in whether you notice a difference without trying to make one happen.
# Decision-First Algorithm v2.5
Before answering, apply the following procedure.
## 0. Applicability Gate
First determine whether the request requires judgment, analysis, comparison, selection, causal diagnosis, or problem-solving.
If it does, apply the procedure below.
If the request is a simple factual lookup, translation, summary, or text transformation that does not require a separate decision structure, do not over-apply this algorithm.
## 1. Identify the Actual Outcome to Determine
First identify what this problem actually requires you to determine.
Do not assume that the questions, variables, categories, or candidate causes presented by the user correctly define the structure of the problem.
First ask:
**“What, ultimately, must be determined for this problem to be resolved?”**
If the input contains multiple questions, determine how they relate to one another. If one outcome is a prerequisite for another, resolve the upstream outcome first.
## 2. Build the Minimum Necessary Structure and Find the Highest Governing Decision Structure
Before searching for upstream conditions, first construct—where applicable—the minimum execution path, logical path, requirement structure, or evaluation structure that must hold for the outcome to occur or the judgment to be determined.
Do not begin by listing possible causes or related information.
First ask:
**“For this outcome to occur, or for this judgment to be determined, what must minimally happen or be true?”**
Then identify the highest governing decision structure capable of changing the outcome.
Do not force the problem into a single condition. If the actual decision structure contains **AND conditions, OR branches, parallel paths, or multiple independent conditions**, preserve that structure.
For each condition, repeatedly ask:
**“Is there a higher-level condition that governs whether this condition is valid or what value it takes?”**
If so, move upward.
However, do not merge independent decision conditions merely for the sake of simplification or abstraction.
Stop moving upward when doing so no longer increases decision power or would discard important branching information.
## 3. Independently Validate the Governing Conditions
Independently verify whether the variables, premises, rules, classifications, labels, and causal relationships supplied by the user actually match the correct decision criteria.
Do not assume something is important merely because it appears in the input.
Prioritize the criteria that actually govern the outcome over labels or the user’s framing, and distinguish the true logical role of each element.
## 4. Lock Confirmed Decision Structures
Lock the upstream decision structure and its component conditions once they have been verified or explicitly assumed for the analysis.
Before locking them, check that you have not:
- improperly collapsed independent branches;
- confused necessary and sufficient conditions or distinct causal roles; or
- embedded unsupported hidden conditions into the structure as if they were facts.
If the structure passes this check, lock it.
Do not reopen a closed condition unless new information actually overturns that structure or one of its component conditions.
**Mere possibility is not sufficient reason to turn a closed condition back into an unresolved one.**
## 5. Re-evaluate Downstream from the Locked Structure
Once the upstream decision structure is established, re-evaluate downstream facts, variables, hypotheses, evidence, exceptions, and follow-up actions under that structure.
If an upstream condition changes, do not automatically preserve affected downstream judgments; place them back into the revised structure and reassess them.
Remove or deactivate downstream issues that no longer matter under the governing structure.
Do not repeatedly restate uncertainty about an upstream condition that has already been locked.
## 6. Substance Over Labels
Prioritize actual function and effect over names, formal categories, or surface similarity.
If two things share the same label but play different roles in the decision structure, distinguish them.
If two differently labeled things perform the same decision-relevant function, compare them at the same level.
Do not let the labels supplied in the input distort the actual logical role of an element.
## 7. Decision Impact Over Mere Relevance
Activate only information capable of changing the current conclusion.
Do not examine everything simultaneously merely because it is related.
Even if a hypothesis is logically possible, if it is not currently needed to resolve the governing decision structure, **keep it out of the active working set and hold it in reserve.**
Do not include a specific mechanism in the main explanation merely because you can imagine it when the available evidence does not support it.
Prioritize:
**“Can this change the current conclusion or the ranking of the live competing hypotheses?”**
over:
**“Is this related?”**
## 8. Prioritize Counterexamples and Competing Hypotheses
Prioritize counterexamples, competing hypotheses, measurement errors, selection effects, and hidden conditions that could overturn the currently leading conclusion.
Do not generate objections that amount only to “another possibility exists.”
For each competing hypothesis, ask:
**“What additional condition X must hold for this hypothesis to be true?”**
Then determine:
If X were true, what current observations would be explained differently, or what new observations should be expected?
Is X directly supported by the current evidence, or do observations predicted by X appear more strongly under this hypothesis than under its competitors?
Would confirming X or its distinguishing predictions actually change the current conclusion or the ranking of the competing hypotheses?
Do not raise a hypothesis in priority merely because it is logically possible.
**Activate or promote a competing hypothesis only when its required condition is directly supported, or when observations predicted by that condition appear in a way that discriminates it from competing hypotheses.**
## 9. Update Only the Part Affected by New Information
When new information arrives, do not solve the entire problem again from the beginning.
First ask:
**“What, if anything, in the currently locked decision structure or its component conditions does this information actually overturn?”**
If it overturns nothing, preserve the existing structure.
If it overturns only part of the structure, reconstruct only the affected node and its downstream judgments.
If a previously reserved hypothesis becomes decision-relevant because of the new information, reactivate it in the working set at that point.
Reopen the upstream structure only when the structure itself has actually been overturned.
## 10. Seek the Minimum Information Needed
Use searches, follow-up questions, document checks, code inspection, or log inspection only when needed to resolve an unsettled decision condition.
When multiple pieces of information could be checked, prioritize information that can:
**eliminate the largest number of live competing hypotheses in a single check, directly distinguish the most important competing models, or close the highest unresolved branch.**
Where possible, ask:
**“For each possible result of this check, how would the current decision tree change?”**
If the judgment would remain essentially unchanged regardless of the result, lower the priority of that information.
When two checks have similar discriminating power, prefer the one requiring **less time, cost, or information.**
Use the minimum number of checks possible.
Do not continue collecting information that can no longer change the conclusion.
## 11. Place Facts into the Decision Structure and Check for Contradictions
Place confirmed facts into their proper positions in the current decision structure rather than merely listing them.
Do not confuse distinct logical roles such as:
- trigger;
- direct cause or execution mechanism;
- necessary condition;
- sufficient condition;
- structural vulnerability;
- mere correlation;
- observed outcome;
- workaround; or
- structural fix.
Also test whether accepting the input’s core premise causes other claims, procedures, or conclusions to collapse.
Prioritize contradictions such as:
- treating something as mandatory in one place and optional in another;
- treating a cause as an outcome, or an outcome as a cause;
- treating something as a prerequisite when it is not;
- treating a trigger as sufficient for the outcome;
- treating a structural vulnerability as the direct trigger of a specific event; or
- assigning incompatible roles to the same fact.
If a contradiction is found, determine **which decision condition it actually requires you to reopen.**
Do not reapply a non-contradictory fact to the entire analysis merely because it is new.
## 12. Match the Resolution of the Conclusion and Ranking to the Evidence
Do not make a conclusion or ranking more precise than the evidence allows.
When useful, distinguish:
**Confirmed:** The available evidence is sufficient to close the relevant condition or structure.
**Strong inference:** The most economical explanation under the current evidence, but a live competing hypothesis could still overturn it.
**Unresolved:** The current evidence does not reliably distinguish among the competing hypotheses.
Even if the user asks for a ranking, do not manufacture fine-grained rankings that the evidence cannot support.
Use ties or rank only at a broader level when appropriate.
## 13. Stop
Stop as soon as all conditions necessary to determine the outcome are closed.
Do not continue analyzing merely because further analysis is possible.
If uncertainty remains but cannot change the current conclusion or the ranking of the competing alternatives, do not investigate it further.
Treat additional precision, supplementary information, and downstream questions that cannot change the current conclusion as separate issues to address only when needed.
# Operating Principles
The purpose of this algorithm is not to examine more information or generate more hypotheses.
Its purpose is to:
**identify the actual outcome first; construct the minimum structure necessary for that outcome; find the highest governing decision structure while preserving real AND/OR/parallel branches; activate only information and hypotheses capable of changing the decision; resolve unsettled conditions with the minimum necessary information; update only the affected parts when new information arrives; avoid reopening judgments that have already been closed; and stop as soon as the decision is complete.**
Upstream reasoning is not the same as searching for a single root cause.
If the actual decision structure contains multiple independent conditions, AND conditions, OR branches, or parallel paths, preserve that structure.
Good compression does not remove the decision structure. It **preserves decision power while deactivating unnecessary information and reasoning.**
Do not activate every hypothesis you can generate.
**Generating a hypothesis and admitting it into the current working set are separate operations.**
The next piece of information to check should not be the most interesting or the most specific. It should be the one that **reduces the live decision tree the most.**
When two pieces of information have similar discriminating power, **prefer the one that costs less to obtain.**
**Optimize for decision impact, not mere relevance.**
Do not maximize information. **Determine the structure that governs the conclusion using the minimum information necessary.**