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Hallucination

As explained in Financial AI in Practice

A model hallucination is generated content that is unsupported or misleading but presented as though it were reliable.

A hallucination is plausible-sounding but false or unsupported content generated with confidence. Because LLMs generate likely next words rather than retrieve verified facts, an invented CEO quote or misstated financial metric can mislead investment decisions. Treat generated signals as hypotheses to verify, not proof. section 3.1.3 section 11.1.1

Retrieval-Augmented Generation (RAG) can ground responses in trustworthy facts, but it does not eliminate hallucinations. Outdated or irrelevant retrieved documents can still produce wrong answers, so reliable ingestion and retrieval matter. section 3.1.3

Investment research

A model may confidently invent a CEO quote or misrepresent a financial metric. Verify such claims rather than relying on their fluency; source visibility and objective prompt guardrails can support transparency and control. section 11.1.1 section 2.5.2

How Financial AI in Practice covers it

How deep it goes
Defines plausible fabrication and outlines financial failure modes and RAG limitations.
Examples
Cites invented CEO quotes, misstated financial metrics, and misleading positive spins in summaries.
What you can do after
Evaluate generated investment signals as hypotheses to verify rather than proof.
How it connects
Connects hallucinations to large language models, Retrieval-Augmented Generation, retrieval quality, and human oversight.

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