Why You Need a Fact Base—Not Just a Knowledge Base—for Reliable AI

As businesses race to integrate AI, many are still relying on traditional knowledge bases—collections of documents, wikis, and unstructured data. But if you want AI that’s accurate, auditable, and business-ready, you need a Fact Base with robust fact control, not just a knowledge base. Here’s why.

Fact Base vs. Knowledge Base: The Core Difference

Knowledge Base (RAG-based):

• Built on Retrieval-Augmented Generation (RAG) models.
• AI “searches” for relevant chunks from a pool of documents, then tries to answer your question by stitching together snippets.
• Prone to ambiguity, outdated information, and “hallucinations”—AI confidently generating plausible but wrong answers.

Fact Base:

• A curated, structured set of atomic facts—clear, unambiguous statements about your business.
• Each fact is explicit, up-to-date, and controlled.
• AI references these facts directly, reducing error and hallucination.
• Designed for precision, auditability, and trust.

Why Fact Control Matters

As highlighted in our recent article, the key to trustworthy AI is Fact Control:

• Single Source of Truth: NOAN’s approach is to break your business knowledge into “Smart Blocks”—each an individual fact. There’s only ever one version of a fact at a time, ensuring clarity and consistency.
• Auditability: You know exactly what the AI is referencing, and you can trace back to who changed what, and when.
• Granular Permissions: Decide who can add, edit, or approve facts, so your AI never references outdated or incorrect information.
• Contextual Accuracy: Facts are tagged and linked, so AI understands relationships and relevance.

Without fact control, your AI is guessing. With it, your AI is referencing the same truth your team relies on.

What Should a Fact Base Include?

A robust Fact Base should cover:

• Core Business Data: Mission, vision, product specs, pricing, policies.
• Operational Facts: Workflows, team roles, key contacts, compliance requirements.
• Market Intelligence: Competitor facts, customer segments, regulatory changes.
• Historical Decisions: Why certain choices were made, with supporting facts.

Each fact should be:

• Atomic: One idea per fact.
• Verifiable: Linked to a source or owner.
• Updatable: Easy to revise as your business evolves.

Key Takeaways from the Fact Control Article

• AI is only as good as its source material. If you feed it a soup of half-truths and outdated docs, you’ll get “FUBAR Mode”—confident, plausible nonsense.
• Fact control is non-negotiable. Businesses need to treat facts as assets, not afterthoughts.
• A Fact Base is the foundation for trustworthy AI. It’s how you move from “AI as a toy” to “AI as a business-critical tool.”

If you want AI that’s accurate, auditable, and aligned with your business, you need a Fact Base—not just a knowledge base. Fact control is the difference between AI that helps and AI that hallucinates. Build your Fact Base, and you’ll build AI you can trust.

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