How to Adapt This Project to Other Domains
The structure demonstrated in the major purchase example is not domain-specific. What changes from one use case to another is content, not method. The same project architecture can be reused wherever AI is asked to support decisions, analysis, or long-horizon thinking—provided the roles of authority, evidence, and judgment remain explicit.
Adaptation begins by identifying what kind of problem is being solved, not by changing the file structure.
Step 1: Identify the Decision Type
Before adapting the project, classify the problem:
Decision support (choosing among options with consequences)
Analytical evaluation (assessing claims, risks, or scenarios)
Exploratory inquiry (understanding a complex or unfamiliar domain)
The project structure remains the same, but the required level of rigor, sourcing, and confidence expectations scale with consequence.
If the outcome could plausibly be defended or challenged later, treat it as decision-grade.
Step 2: Rewrite the Objective, Not the Files
Do not invent new file types prematurely.
Instead, rewrite:
the Objective & Decision Definition, and
the Constraints and Non-Goals.
If these are well-formed, the remaining files—assumptions log, facts vs inference, audit, and decision summary—transfer cleanly.
If adaptation requires changing the file structure, the objective is likely under-specified.
Step 3: Redefine What Counts as a “Fact”
Different domains impose different standards of evidence.
For example:
In financial planning, facts may be regulatory rules, historical rates, or account terms.
In health-adjacent decisions, facts may be consensus guidelines or well-documented studies.
In governance or policy analysis, facts may include statutes, official records, or procedural rules.
The separation between fact, inference, and speculation remains invariant.
Only the evidentiary bar changes.
Step 4: Adjust the Assumptions Log, Not the Analysis Tone
Every domain has its own default assumptions. These should be surfaced, not absorbed.
Examples:
Career decisions often smuggle in assumptions about fulfillment or growth.
Financial decisions often assume rational markets or stable conditions.
Governance decisions often assume good faith or procedural compliance.
Do not soften the method to accommodate these assumptions. Log them, challenge them, and accept or reject them explicitly.
The tone of analysis should remain neutral and inspectable.
Step 5: Recalibrate Confidence Expectations
Confidence must scale with uncertainty and irreversibility.
High-reversibility domains tolerate lower rigor and lower confidence penalties.
High-consequence or low-reversibility domains demand explicit uncertainty and conservative confidence assignment.
Do not force high confidence to satisfy closure pressure.
A defensible low-confidence decision is preferable to a confident but fragile one.
Step 6: Preserve the File Authority Rules
The authority vs. reference distinction applies in every domain.
Reference material—prior work, articles, expert opinions—must not silently become constraint. If it governs reasoning, it must be promoted explicitly into an authoritative file.
This rule prevents legacy thinking from masquerading as current judgment.
Examples of Direct Transfer
The same project structure can be used for:
choosing between job offers,
evaluating a financial strategy,
selecting a certification or education path,
assessing a policy proposal,
planning a major life change with multiple trade-offs.
In each case, only the content of the files changes.
The discipline does not.
Final Principle
Do not adapt the method to fit the domain.
Adapt the domain to fit the method.
If the structure feels burdensome, that is often a signal that the decision is under-defined or that uncertainty is being resisted rather than managed.
The project framework exists to make that resistance visible.