@noah-grantfinance-strategy-evaluation-redteamTextePublicMis à jour le 14 juin 2026

Finance Strategy prompt that builds an evaluation suite for high-risk AI workflows and returns eval matrix, adversarial cases, grading rubric, and release threshold.

AI Workflow Finance Forecasting FP&A Strategy

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Prompt

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# Role
You are a finance strategist who turns messy operating data into decision-grade narratives. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Build an evaluation suite for high-risk AI workflows for AI-assisted financial planning, scenario analysis, investor memos, and board reporting.

# Inputs
- Project context: a burn multiple and runway analysis for a Series B AI company
- Target audience: FP&A teams, founders, investors, strategy leads
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: spreadsheet model, accounting export, CRM forecast, BI dashboard
- Constraints: state assumptions; separate actuals from forecast; show sensitivity ranges
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- P&L exports
- headcount plan
- pipeline forecast
- board questions

# Known risks to control
- false precision
- hidden one-time costs
- forecast leakage
- unexplained variance

# Method
1. Convert user outcomes into measurable tasks
2. Include realistic failure modes
3. Separate correctness from style
4. Define pass, warn, and block thresholds

# Required output sections
1. Success criteria
2. Golden tasks
3. Adversarial tasks
4. Rubric
5. Sampling plan
6. Release decision

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artefacts

1 artefacts

Example Output: Finance Strategy Evaluation and Red-Team Harness

Inputs used

Project context: a burn multiple and runway analysis for a Series B AI company
Target audience: FP&A teams, founders, investors, strategy leads
Success metric: activation, quality, and risk reduction
Available tools and data: spreadsheet model, accounting export, CRM forecast, BI dashboard
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

eval matrix, adversarial cases, grading rubric, and release threshold

Success criteria

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting false precision. A passing result must cite the evidence source and state confidence.

Golden tasks

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting hidden one-time costs. A passing result must cite the evidence source and state confidence.

Adversarial tasks

Use pipeline forecast as evidence, apply the constraint "show sensitivity ranges", and explicitly note how the plan reduces forecast leakage. The output should be ready for a practitioner to act on without a follow-up explanation.

Rubric

Use board questions as evidence, apply the constraint "state assumptions", and explicitly note how the plan reduces unexplained variance. The output should be ready for a practitioner to act on without a follow-up explanation.

Sampling plan

Release in three gates: internal dry run, limited pilot, then measured expansion. Each gate must show evidence that separate actuals from forecast is true in practice, not only in documentation.

Release decision

Release in three gates: internal dry run, limited pilot, then measured expansion. Each gate must show evidence that show sensitivity ranges is true in practice, not only in documentation.

Recommended Decision

Proceed with a narrow pilot focused on P&L exports and headcount plan. Treat false precision as the primary launch blocker. The first milestone should prove that the workflow produces a usable driver model narrative, scenario table, and board-ready recommendation with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted financial planning, scenario analysis, investor memos, and board reporting.
It includes the required sections: Success criteria, Golden tasks, Adversarial tasks, Rubric, Sampling plan, Release decision.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: false precision.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Finance Strategy: Evaluation and Red-Team Harness

Use this prompt when you need eval matrix, adversarial cases, grading rubric, and release threshold for AI-assisted financial planning, scenario analysis, investor memos, and board reporting.

Best for

FP&A teams, founders, investors, strategy leads
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.