Definition · AI in finance
Prompt engineering
Prompt engineering is the practice of designing instructions, examples, constraints, and context so an AI model produces more useful outputs. For prompt 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 prompting, prompt design
Why it matters
Understanding prompt 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 handles the prompting internally and grounds it in your semantic model, so finance teams ask questions in plain language instead of engineering prompts.
In practice
Governance example
Teams use prompt 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 handles the prompting internally and grounds it in your semantic model, so finance teams ask questions in plain language instead of engineering prompts.
In practice, teams should define prompt engineering with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding prompt 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 handles the prompting internally and grounds it in your semantic model, so finance teams ask questions in plain language instead of engineering prompts.
A strong workflow for prompt 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 handles the prompting internally and grounds it in your semantic model, so finance teams ask questions in plain language instead of engineering prompts.
FAQ
What is prompt engineering?
Prompt engineering is the practice of designing instructions, examples, constraints, and context so an AI model produces more useful outputs. For prompt 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.
What is the difference between prompt engineering and context engineering?
The boundary for prompt engineering differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what prompt engineering is, the techniques used to shape model behavior through input, and how it differs from context engineering, so teams should compare those boundaries before using it in reporting or planning.