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Why Probabilistic AI Fails (in High-Stakes Work)

Failure mechanics

Plausibility is not epistemic validity.

Next-token prediction is a powerful compression engine. In high-stakes work, its core risk is not “inaccuracy” — it’s unverifiable confidence.

Constraints & SHACL The three laws Governance approach

The illusion

LLMs are excellent at generating text that resembles correct answers. But resemblance is not the same as truth.

In practice, fluency can mask missing sources, missing constraints, and missing causal structure.

Why RAG helps — and why it still fails

Causal questions

“Why did X happen?” requires mechanisms and context, not just relevant passages.

Exceptions and footnotes

Policies and regulations live in edge cases. Retrieval often misses the clause that flips the decision.

Cross-document constraints

“This is allowed only if A and B and not C” is a constraint problem. Text similarity doesn’t enforce it.

Long-range dependencies

High-stakes reasoning often spans multiple documents, time windows, and conditional rules.

What changes with glass-box systems

Traceable path

The system shows the reasoning path it took — not just a final answer.

Explicit sources

Every claim has provenance (where it came from, why it was selected).

Enforced constraints

Constraints are gates. If a constraint fails, the system refuses or escalates.

If the system can’t provide path + sources + constraints, it must abstain. This is not a UX preference — it’s an architectural constraint.

Diagram: plausible text vs decision-grade pipeline

flowchart LR;
    U["User request"] --> L["LLM output"];
    L --> P["Plausible answer"];
    P --> R1["Risk: confident fabrication"];

    U --> G["Governed system"];
    G --> C["Constraint checks"];
    C -->|"Fail"| A["Abstain / escalate"];
    C -->|"Pass"| E["Evidence retrieval"];
    E --> T["Trace + provenance"];
    T --> O["Answer + audit trail"];

Diagram: where RAG fails

graph TD;
    R["Retrieval" ] --> S["Selected snippets"];
    S --> L["LLM synthesis"];
    X["Cross-doc constraints" ] -.->|"often missing"| L;
    E["Edge-case clause" ] -.->|"often missed"| S;
    M["Mechanism / causal model" ] -.->|"not guaranteed"| L;
    L --> O["Output" ];