Preventing AI Hallucinations with In-House Models

When a new employee joins a company, their success largely depends on how they’re trained. Ideally, they learn from the best sources: mentors, structured training programs, and official company policies. But what happens when they hang around the wrong people and absorb misinformation? Their decisions, behaviors, and contributions become misaligned with the organization’s goals. However, the potential for improper training isn’t a reason not to hire someone who would be a great asset to the team.

The same principle applies when training large language models (LLMs). If an AI model is trained on irrelevant, outdated, or unreliable data, the model will start producing misleading or incorrect responses (what we now call AI hallucinations).

Organizations often cite hallucinations as a key reason why they’re hesitant to adopt AI. But the real issue isn’t AI itself. It’s the level of control over the data it learns from.

Understanding AI Hallucinations

AI hallucinations occur when an LLM generates incorrect or misleading information. One AI startup even has a leaderboard tracking how often different LLMs hallucinate.

AI hallucinations typically arise from two primary factors:

1. Initial Training Data – Publicly available AI models are trained on vast, diverse datasets, including information from the internet, which may be inaccurate, biased, or outdated.
2. Aggregated User Feedback – Many models incorporate user feedback to refine responses, but if feedback is inconsistent or incorrect, it can reinforce hallucinations over time.

AI hallucinations happen regularly, sometimes in subtle ways. One AI startup even has a leaderboard tracking how often different LLMs hallucinate. It’s critical to continue to verify outputs. While hallucinations aren’t 100% avoidable, they are manageable.

How Companies Can Prevent AI Hallucinations

AI hallucinations are not a reason to avoid AI adoption. For companies serious about adopting AI, building custom models can be more beneficial than relying on publicly available tools with uncontrolled data sources. Here’s how companies can prevent hallucinations with in-house models:

1. Isolate and Train AI on Internal Data

Running AI models on internal servers or in a private cloud environment (e.g., AWS, Azure) provides more control over training data. By feeding AI well-organized, company-approved data, hallucination risk goes down as the model is not exposed to potentially conflicting or incorrect information.

2. Fine-Tune AI on Verified Data

Instead of relying on broad datasets, companies should fine-tune AI models using:

• Structured databases (e.g., ERP, CRM, HR systems)
• Internal documentation and policies
• Engineering specifications and customer requirements

Fine-tuning AI on verified data means reinforcing the right information while filtering out the noise. For example, imagine data sitting in the ERP system. Some of it is useful, some of it is not. Training the AI model involves teaching it what is reliable. If you're pulling in unstructured data from a proposal library, some of those proposals may have been drafts that were never finalized or approved, but the AI model doesn’t know that. Maybe the winning proposal is applicable to current projects, but the drafts are irrelevant.

3. Implement Guardrails for Model Responses

AI models should be configured to:

• Flag and avoid answering questions when confidence is low.
• Provide citations or references for factual claims.
• Be retrained when incorrect answers are identified.

Part of this involves testing and running models to understand their behavior. For example, if you're using AI to generate a bill of materials for a custom-manufactured compressor, the AI must reference the correct specifications and sources for each component. Just having AI generate a bill of materials isn’t enough. You must be able to trace where that information came from, check assumptions, and potentially retrain the model to improve accuracy over time.

4. Utilize Retrieval-Augmented Generation (RAG)

RAG is an AI framework that allows models to retrieve real-time data rather than relying solely on static training datasets. Connecting AI models to up-to-date internal information sources helps companies improve accuracy and reduce reliance on outdated or incorrect knowledge.

For example, RAG can continuously pull customer interactions to inform proposal outputs to ensure they align with recent customer discussions. Similarly, RAG can be used to pull data from IoT devices on a shop floor to offer real-time updates on production time, and material availability.

5. Establish AI Governance and Human Oversight

Companies need an AI governance framework that includes:

• Clear policies on AI training and use.
• Regular auditing and quality checks.
• A human-in-the-loop process for decision-making.

AI governance includes well-defined policies and procedures that specify what AI can and cannot be used for. Organizations should establish guidelines for how AI is fact-checked and validated. For instance, a company may implement a quality control process where every AI-generated proposal undergoes human review before it reaches customers. Governance also extends to ethical AI use to prevent unintended risks or errors that could impact customer relationships or compliance.

A Case for In-House AI Development

Don’t rush to adopt AI-powered software without considering how the models behind these tools work. While AI integrations in SaaS products can be useful, they often function as glorified versions of ChatGPT. They lack deep customization for company-specific needs. Companies that want a competitive advantage should consider hiring in-house AI specialists or contracting experts to build custom AI models aligned with business goals.