Definition · AI in finance
Context engineering
Context engineering is designing what information a model receives at inference time. For context engineering, 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 context curation, context management
Why it matters
Understanding context engineering 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 does the context engineering for you: it assembles the right context from a connected ontology so the model works from your system of record, not generic text.
In practice
Governance example
Teams use context engineering 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 does the context engineering for you: it assembles the right context from a connected ontology so the model works from your system of record, not generic text.
In practice, teams should define context engineering with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding context engineering 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 does the context engineering for you: it assembles the right context from a connected ontology so the model works from your system of record, not generic text.
A strong workflow for context engineering 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 does the context engineering for you: it assembles the right context from a connected ontology so the model works from your system of record, not generic text.
FAQ
What is context engineering?
Context engineering is designing what information a model receives at inference time. For context engineering, 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 is context engineering different from prompt engineering?
The boundary for context engineering differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what context engineering is — designing what information a model receives at inference time — and how it has expanded beyond prompt engineering, so teams should compare those boundaries before using it in reporting or planning.