About Session
As finance teams adopt AI copilots for reporting and analysis, three questions keep coming up: how do you prompt effectively, how do you trust the output, and how do you trace the output back to the source data. This session walks through practical answers to all three, using FinanceOS as the working example. Attendees will leave with a repeatable framework they can apply to any AI tool in their finance stack.
Learning Objectives
- Prompting best practices for financial data: Learn what data to feed an LLM so outputs are accurate and relevant. This covers structuring context, avoiding common data leakage or ambiguity issues, and tailoring prompts for financial statements, variance analysis, and forecasting tasks.
- Validating AI outputs before you rely on them: Learn a practical checklist for confirming an AI-generated number or narrative is correct before it goes into a report or gets shared with leadership. This includes cross-checking against source data and recognizing common failure modes in LLM outputs.
- Maintaining an audit trail for AI-assisted work: Learn how to document data lineage and decision trails when AI tools are involved in financial reporting, so the process holds up under audit or internal review.
