Definition · AI in finance
Retrieval-augmented generation
Retrieval-augmented generation is retrieving relevant documents at query time to ground an LLM's output. For retrieval-augmented generation, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as RAG, retrieval augmented generation
Why it matters
Understanding retrieval-augmented generation matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo grounds answers in a connected ontology and deterministic compute, not vector retrieval over documents, so finance figures are computed and reconciled rather than retrieved.
In practice
Governance example
Teams use retrieval-augmented generation when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo grounds answers in a connected ontology and deterministic compute, not vector retrieval over documents, so finance figures are computed and reconciled rather than retrieved.
In practice, teams should define retrieval-augmented generation with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding retrieval-augmented generation matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo grounds answers in a connected ontology and deterministic compute, not vector retrieval over documents, so finance figures are computed and reconciled rather than retrieved.
A strong workflow for retrieval-augmented generation separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo grounds answers in a connected ontology and deterministic compute, not vector retrieval over documents, so finance figures are computed and reconciled rather than retrieved.
FAQ
What is retrieval-augmented generation?
Retrieval-augmented generation is retrieving relevant documents at query time to ground an LLM's output. For retrieval-augmented generation, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
What is the difference between RAG and a connected ontology?
The boundary for retrieval-augmented generation differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what RAG is — retrieving relevant documents at query time to ground an LLM's output — its retriever-plus-generator structure, and its limits for numeric data, so teams should compare those boundaries before using it in reporting or planning.
Sources
- RAG for Finance: Automating Document Analysis with LLMs CFA Institute Research and Policy Centerrpc.cfainstitute.org
- What is RAG (Retrieval-Augmented Generation)? Amazon Web Services https://aws.amazon.com › ... › Generative AIaws.amazon.com
- What is RAG (Retrieval Augmented Generation)? IBM https://www.ibm.com › think › topics › retrieval-augme...ibm.com