Question 7 of 34
How do we handle AI-generated content and hallucinations?
Published by AI Governance Institute · Practical Governance for Enterprise AI
Defining responsibility when AI produces inaccurate outputs used in contracts, reports, or customer communications, and the controls that prevent harm.
If you only do 3 things, do this:
- 1.Require human review of all AI-generated content before it is used externally. Define what "review" means: scanning for obvious errors is not the same as verifying factual claims.
- 2.Use retrieval-augmented generation (RAG) for any use case where factual accuracy is required. Parametric memory alone is not sufficient.
- 3.Train employees to treat AI outputs as drafts requiring verification. The "good enough" AI culture is the biggest source of hallucination-related risk in enterprise settings.
The Situation
Who this is for: Legal, compliance, and operations teams that use generative AI for drafting, research, or customer communications
When you need this: Before deploying any generative AI in a context where output accuracy matters — contracts, regulatory filings, customer communications, professional advice
The Decision
What controls do we need so that AI-generated content meets our accuracy and liability standards before it leaves the organization?
The Steps
- 1Map all generative AI use cases by risk level: external communications, regulatory filings, and legal documents are highest
- 2For each high-risk use case, define the required review standard (scan vs. verify vs. independent rewrite)
- 3Implement RAG for use cases requiring factual grounding; document which source documents each output draws from
- 4Build output flagging for content making specific factual claims, dates, citations, or statistics
- 5Establish a logging system for AI-generated content used in significant decisions or communications
- 6Roll out training defining AI output as a draft and specifying what verification is required before use
The Artifacts
- —AI content risk tiering matrix (use case → risk level → required review standard)
- —RAG implementation checklist
- —AI output logging template (content, use, reviewer, verification method)
- —Employee training scenario library (hallucination examples with correct handling)
The Output
A documented content governance process with defined review standards for each use case, logging in place, and employees trained on their verification obligations.
Hallucinations are a design characteristic, not a bug
Large language models generate plausible-sounding text by predicting likely continuations of input sequences. They do not retrieve verified facts. They can produce outputs that are grammatically fluent, contextually appropriate, and entirely false. This is not a temporary limitation that will be engineered away. It is a fundamental characteristic of how current generative AI systems work.
Organizations that deploy generative AI without controls to detect and prevent hallucinations are accepting liability for outputs they cannot predict or verify. When those outputs appear in contracts, regulatory filings, customer communications, or legal documents, the exposure is significant.
Responsibility and liability
The legal question of who bears responsibility for AI-generated errors is still being resolved in courts and regulators' offices. The emerging consensus is that the deploying organization, not the AI vendor, bears primary responsibility for outputs used in its operations. Terms of service for most major AI platforms disclaim liability for output accuracy and prohibit reliance on AI outputs in high-stakes decisions without human review.
In professional services, attorneys who submitted AI-generated court filings containing fabricated case citations have faced sanctions. In financial services, AI-generated research that contains material errors may implicate securities regulations. The professional and regulatory standards that apply to human-generated work generally apply equally to AI-assisted work product.
Controls that reduce risk
For high-stakes use cases, require human review of all AI-generated content before it is used externally. Define what "review" means in practice: a reviewer who scans for obvious errors is not the same as one who verifies every factual claim against source documents.
Use retrieval-augmented generation (RAG) architectures that ground model outputs in specific, verified documents rather than relying on parametric memory. Implement output filtering that flags content making specific factual claims for additional review. Maintain logs of AI-generated content used in significant decisions or communications.
Train employees to treat AI outputs as drafts requiring verification, not finished work product. The cultural norm that AI output is "good enough" is one of the most significant sources of hallucination-related risk in enterprise settings.
Governance Controls
Operational controls that implement the guidance in this playbook.