Definition · AI in finance
Large language model
A large language model is a neural network trained on large text datasets to understand, summarize, and generate language. For large language model, 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 LLM, large language models
Why it matters
Understanding large language model 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 uses an LLM to decide what to ask, then computes every number with a deterministic engine — so the model interprets, but never invents, the figures.
In practice
Governance example
Teams use large language model 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 uses an LLM to decide what to ask, then computes every number with a deterministic engine — so the model interprets, but never invents, the figures.
In practice, teams should define large language model with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding large language model 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 uses an LLM to decide what to ask, then computes every number with a deterministic engine — so the model interprets, but never invents, the figures.
A strong workflow for large language model 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 uses an LLM to decide what to ask, then computes every number with a deterministic engine — so the model interprets, but never invents, the figures.
FAQ
What is a large language model?
A large language model is a neural network trained on large text datasets to understand, summarize, and generate language. For large language model, 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.
How are LLMs used in finance?
To use large language model, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.