This article was originally published on LinkedIn.

With each NetSuite AI prompt engineering project I take on, I try to learn and improve my techniques. And over the past few months, one breakthrough has reshaped the way I approach AI-driven financial analysis: the quality of the output has far less to do with what you ask for, and far more to do with the governance you embed into the prompt.

Most prompts focus on instructions. Very few address controls. What I've found is that if you want serious, reliable, repeatable, CFO-quality results, you need both.

That insight pushed me to rethink how I design prompts. Today, I treat governance - lineage, validation, risk checks, assumption logging, escalation rules, and auditability - as first-class citizens inside the prompt itself. When you build prompts this way, you stop getting "answers" and start getting systems.

This guide is my blueprint for adding governance to any type of prompt. Finance, operations, sales, HR, supply chain, forecasting, reporting - it doesn't matter. Governance is universal, and once you bake it in, the quality and trustworthiness of the output improves immediately.

In this article, I'll show you how to build governance into your prompts.

A Working Example

Before we get into the "how," I want to show you a real example of the kind of governance I'm talking about.

Here's a "Working Capital Optimization" prompt that demonstrates the full lineage, validation, risk controls, and governance structure covered in this guide.

And here's an example of the output that prompt produces - a governed, lineage-aware, CFO-grade working capital analysis.

Everything in this guide - lineage entries, verification tests, assumptions logs, risk models, governance summaries - appears in these two examples. If you skim them before reading further, the rest of the framework will make a lot more sense.

Install a Professional Mindset

Before you add governance, you need to anchor the model in a role that inherently values it.

For example:

"You are a Senior FP&A Analyst who operates with CFO-grade rigor and explicit risk-management discipline."

or

"You are a QA Lead whose work must withstand audit, review, and regulatory standards."

Why this matters:

  • Identity determines behavior
  • Behavior determines the level of discipline
  • Discipline determines whether governance is natural or forced

A model acting as "a helpful AI assistant" will never produce the same rigor as one acting as "a senior analyst presenting to the board."

Governance starts with mindset.

Define An Analytical or Operational Framework

Most prompts assume the model knows the steps required. It doesn't.

To embed governance, you must define the pipeline:

  • The stages
  • The required analyses
  • The intermediate outputs
  • The sanity checks
  • The structure

This transforms the model into a system that cannot skip critical thinking.

Here's an example of the structure I often use:

  1. Diagnose
  2. Quantify
  3. Validate
  4. Recommend
  5. Document assumptions
  6. Assess risk
  7. Produce deliverable

By forcing this type of sequence, you reduce - and in some cases eliminate - the randomness in output quality.

Governance loves structure. So give it structure.

Add Lineage Requirements

In traditional FP&A or accounting work, lineage is just part of professional hygiene. If a CFO asks, "Where did this number come from?" you better have an answer - ideally with a spreadsheet tab, query, or report behind it.

AI, by default, doesn't have that discipline. It will give you answers, but it won't tell you how it got them.

That's where prompt-level lineage comes in.

Lineage gives you:

  • traceability
  • auditability
  • transparency
  • non-hallucination enforcement

Lineage looks like this:

For every dataset used, the model should produce a table that includes:

  • Lineage ID
  • Type
  • Source / Query
  • Filters
  • Purpose
  • Completeness

Then require:

"Every claim, metric, or recommendation must reference at least one Lineage ID."

This dramatically improves output integrity. It helps ensure that the model won't invent numbers, because it must name its sources. It can't blur reasoning, because it must be traceable. And it can't shortcut analysis, because missing lineage becomes an obvious flaw.

Lineage is governance's foundation.

Require a Verification Test Plan

Once you have lineage, the next layer is verification.

Have the model produce (and follow) test cases:

Examples:

  • Does AR Aging total tie to GL AR?
  • Do scenario models recalculate correctly?
  • Do aging buckets categorize correctly?
  • Do metrics match definitions?

Every test should include:

  • Test ID
  • Purpose
  • Steps
  • Expected Result
  • Priority
  • Pass/Fail

When you force the model to validate itself recommendations become evidence-based, errors become visible, the analysis becomes trustworthy, and the user gains confidence.

Verification turns output from "likely correct" to "proven correct."

Add a Risk & Confidence Framework

Keep in mind that governance isn't about getting perfect results. It's more about knowing where the uncertainty in the results is.

To accomplish that, you should require:

  • risk scoring
  • materiality assessment
  • data reliability rating
  • confidence level
  • HITM (Human-in-the-Middle) flags

Define a matrix like this:

  • Dimension
  • Rating
  • Rationale
  • Key Risks

This forces the model to acknowledge uncertainty, highlight missing data, identify high-risk assumptions, and escalate things where it's needed.

When an AI system admits what it doesn't know, the quality of what it does know increases dramatically.

Require an Assumptions Register

The assumptions that a model makes are invisible unless you force them into the light.

To make them visible, add this rule:

"Document every assumption made, categorized by data, business logic, or methodology."

Then format the assumptions like this:

  • Assumption ID
  • Description
  • Category
  • Rationale
  • Sensitivity
  • Impact if Wrong

This gives the user a transparent view of model reasoning, the ability to challenge assumptions, a way to validate or override logic, and a clear record for audit purposes.

Assumptions are where bad analysis hides. This technique helps you expose them.

Add Escalation & HITM Requirements

AI shouldn't replace human judgment. It should elevate it.

To do that, you should add "Human in the Middle" (HITM) escalation rules such as:

  • "If data completeness < 80%, stop and request confirmation."
  • "If AP terms vary by >3 standard deviations, escalate."
  • "If confidence < 60%, mark recommendation as provisional."
  • "If lineage has missing datasets, highlight before proceeding."

This turns the prompt into a system that knows when to ask for help, which is a critical governance feature.

Force the Deliverable to Follow Structured / Repeatable Patterns

A governed process must produce governed outputs.

Therefore, you should require:

  • consistent section headings
  • standard tables
  • clear narrative patterns
  • embedded citations
  • explicit KPI definitions
  • consistent recommendation formatting

This turns the prompt into a reusable template rather than a one-off execution.

The benefit of this technique is that you get the same shape of deliverable every time. That's exactly what businesses need and what they expect.

Close With a Governance Summary

At the end of the deliverable, require a short governance summary:

  • What data was used
  • What data was missing
  • What tests passed/failed
  • What assumptions were critical
  • What risks remain
  • What needs human review

This is the AI equivalent of "financial footnotes." It completes the governance loop.

I've found that this summary is best provided as appendices to the output.

Putting It All Together

Here's the full pattern that I'm now using in my most advanced prompts:

  1. Identity & Mindset
  2. Analytical/Operational Framework
  3. Data & Scope Requirements
  4. Lineage Structure
  5. Verification Test Plan
  6. Assumptions Register
  7. Risk & Confidence Model
  8. HITM / Escalation Logic
  9. Deliverable Structure
  10. Governance Summary

Each layer tightens the behavior of the model, improves reliability, reduces ambiguity, and creates auditability.

By using this method, you're not so much creating prompts as you are building governed analytical systems.

Wrapping Up

As AI becomes more embedded in how companies analyze performance and make decisions, the discussion is shifting. The big question is no longer whether AI can run the analysis - that part is settled.

The real question is: "Can we trust what it produces?"

That's why governance matters. Lineage, validation, assumptions, risk checks, escalation rules aren't "extras." They're what separate a clever AI answer from a disciplined, audit-safe, CFO-ready analysis.

And the time to add governance isn't after the model generates output. It's before the model ever begins.

Good prompts tell AI what to do.

Governed prompts teach AI how to think.