Inferencing Paths

#	Inferencing path	Best used when	Trigger signal	Recommended primitives
1	Direct inference	The ask is simple, bounded, and low-risk	Clear question; familiar pattern	Point, line, bound
2	Decomposition inference	A messy issue needs to be separated into parts	Many variables; unclear structure	Points, circles, triangle
3	Comparative inference	Options, models, or paths need weighing	“Which is better?” / “What differs?”	Triangle, line, VET
4	Causal inference	You need to explain why something happens	Recurring symptom; pattern; failure	Line, triangle, VET
5	Constraint inference	The answer depends on limits	Budget, time, legality, capacity, reversibility	Circle, triangle, exception marker
6	Exception inference	A case does not fit normal routing	Rare, ambiguous, high-consequence, edge-case	Exception marker, escalation path, accountability bound, VET
7	Adversarial inference	The idea must survive gaming, misuse, or capture	Governance, security, incentives, politics	WHATIF, triangle, boundary stress-test, VET
8	Second-order inference	Consequences matter beyond the first move	“What happens after?” / systemic effects	Line, circle, feedback loop, VET
9	Counterfactual inference	You need to test alternative paths	“What if we did not do this?”	WHATIF, branch line, reality-touch
10	Reality-touch inference	The model is coherent but untested	Logic sounds good but needs grounding	VET, field probe, consequence check
11	Compression inference	The work must become reusable	Long thread; many insights; repeated pattern	Compression primitive, bound, trigger, failure mode
12	Generative inference	The goal is to create new usable possibilities	Open design, strategy, product, framing	WHATELSE, triangle, circle expansion, recombination
13	Human-supervised oscillatory inference	The problem is ambiguous, systemic, high-context, or value-bearing	Need for self-check, cross-check, human sense-check	Self-reference, cross-reference, human judgment bound, VET, reality-touch
14	Large-data chunking to DSE inference	Large datasets, documents, knowledge bases, or corpora must become usable	Too much data to read directly; need traceable synthesis	Chunk, metadata, knowledge graph, DDC, DSE, VET, compression bound

#	Inferencing path	Expected output	Example
1	Direct inference	Direct answer	“What is the difference between adoption and implementation?”
2	Decomposition inference	Component map	Breaking GenAI adoption into leadership, workflow, data, capability, and governance layers
3	Comparative inference	Trade-off table or ranked options	Comparing centralized AI governance vs federated AI enablement
4	Causal inference	Cause chain / leverage points	Why a GenAI pilot gets high excitement but low sustained usage
5	Constraint inference	Feasible path within limits	Designing a GenAI rollout under limited budget, weak data quality, and low executive patience
6	Exception inference	Exception route	A chatbot gives a legally risky answer that cannot be handled as a normal support ticket
7	Adversarial inference	Failure modes and countermeasures	Testing how employees might game AI-assisted performance scoring
8	Second-order inference	System effects map	If AI reduces support workload, what happens to roles, incentives, trust, and future capability?
9	Counterfactual inference	Scenario set	What if the organization delays GenAI adoption by two years?
10	Reality-touch inference	Validation test or correction	Testing a change primer with 20 stakeholders before scaling it enterprise-wide
11	Compression inference	Reusable operating phrase / token candidate	Turning a long discussion into a CUT (compressed usable token) like “Automate flow, not accountability”
12	Generative inference	New options / concept set	Generating possible post-chatbot interfaces beyond search, chat, dashboard, or agent
13	Human-supervised oscillatory inference	Reframed and stress-tested inference	Iterating a GenAI governance thesis through model (may use several models) critique, stakeholder views, and human judgment
14	Large-data chunking to DSE inference	Usable DSE output from large data	Turning books, long reports, thousands of tickets, change logs, or stakeholder comments into traceable themes, risks, and action signals