AI for RWA Research

AI-assisted educational manuscript. The workflow is a teaching design. It does not imply that this site has a connected model provider or production research agent.

Learning objectives

• Trace a research conclusion to a specific source passage and revision.
• Separate extraction, inference and unsupported assertion.
• Design a review that measures evidence quality rather than fluent prose.

Assistance needs an evidence boundary

An AI-assisted research process can be designed to suggest questions, extract candidate fields or draft a comparison. The output should remain a proposal until its material claims are checked. A plausible sentence is not evidence of a right, fee, investor qualification or working redemption route.

NIST's AI Risk Management Framework organizes risk work through Govern, Map, Measure and Manage. Governance spans the lifecycle; these functions are not a sequence that produces a certification. Use the framework to define responsibility, context, evaluation and responses for the intended research task. NIST: AI RMF, NIST: Core

The proposed workflow here has no live model connection and makes no claim of NIST approval or measured model accuracy.

Worked example: a summary that strengthens a promise

Imagine a fictional fund document states: “The manager may distribute available net income, subject to the stated reserve policy.” A candidate summary says, “Holders receive guaranteed monthly income.” The summary introduces at least three unsupported elements: certainty, a monthly schedule and potentially an unconditional payment obligation.

The correct review is not to ask whether the sentence sounds professional. Compare each material phrase with the actual passage. A bounded summary could preserve the discretion, availability condition and reserve-policy reference. If the source does not define timing, the timing field should remain unknown.

Now a second candidate correctly quotes the clause but links an outdated revision. Citation presence alone is insufficient. Record the document identifier, effective date, retrieval date, passage location and content revision. A correct extraction from superseded terms may be wrong for the current product question.

Question → allowed source set and revision
    ↓
Candidate extraction → passage-level evidence → scope and contradiction checks
    ↓
Draft comparison → human review → approved or returned revision

No automatic approval is implied. A reviewer should be able to reject one field without losing the rest of the evidence trail.

Evaluate the right failures

Create a small, explicitly labeled evaluation set with known answers from its source passages. Include missing fees, conflicting effective dates, conditional redemption, ambiguous eligibility, irrelevant documents and a source containing instructions to the assistant. Treat document instructions as source material, not authority to change the research task or reveal information.

Measure factual support, unsupported-claim rate, correct abstention and revision matching. A high proportion of filled fields can be bad if the sources do not contain the answers. Separate retrieval failure from extraction error and interpretation disagreement; they require different remedies.

For named products, use dated primary evidence and the relevant jurisdiction. Do not infer zero fees from absence or turn a document mention into a directly tested feature. Keep automated provenance separate from actual human editorial approval. Model output cannot create an authorized reviewer identity.

Common misconceptions

“Every sentence has a link, so it is supported.” The cited passage may not support the sentence or may be outdated.

“More complete fields mean better research.” Correct unknowns are necessary when evidence is missing.

“An automated critique is human approval.” These are different review sources and must be recorded honestly.

Risks and limits

This chapter provides a proposed research protocol, not a tested AI service. It does not process private contracts, personal financial data or regulated decisions. A real integration would need a justified provider, data boundary, evaluation evidence, cost controls and an authorized review workflow. None is claimed by the manuscript.

Exercise and answer

Exercise: The source says income may be distributed subject to reserves. The model says guaranteed monthly payment. Which parts need correction?

Answer: Remove the unsupported guarantee and monthly schedule, preserve the conditional allocation and cite the governing revision. If timing is absent, mark it unknown.

Related learning

Continue in AI and future finance with document intelligence and controlled decisions. Apply evidence discipline to Research.

Source map

nist-ai-rmf and nist-ai-rmf-core: voluntary U.S. cross-sector framework, directly read by the source-analysis role on 2026-10-18. The clause, candidate output, evaluation design and workflow are original educational examples. No provider, retrieval system or accuracy benchmark was run.