How do I stop AI from making up facts about my business?

Summary

• AI hallucinations occur when language models generate plausible-sounding but factually incorrect information, often because they lack access to ground truth data.
• Databricks Genie prevents hallucinations by grounding AI responses in your actual business data, verified through semantic search over real tables and fields.
• Semantic understanding of your business entities, metrics, and relationships (captured in Genie Ontology) ensures AI stays connected to what's actually true.
• Facts are verified at generation time: Genie retrieves relevant data, translates questions into verified queries, and surfaces only claims it can directly back up.
• Multi-layer governance (Genie Ontology context, semantic search rankings, fact verification) significantly reduces false information without requiring manual review of every answer.

How do I stop AI from making up facts about my business?

Hallucinations happen when AI systems generate confident-sounding answers that are simply false. For business users, this is a critical problem: an agent that answers "Your Q3 revenue was 50 million" when the real number is 45 million erodes trust and breaks decision-making. The root cause is that language models generate text based on statistical patterns, not ground truth. They don't know what's actually true in your business.
Databricks Genie prevents hallucinations through systematic grounding: all responses are anchored to your actual business data, verified through structured queries against real tables. An answer is only returned if Genie can retrieve the facts that support it from your data.

The three layers of hallucination prevention

Layer	How it works	Why it matters
Semantic grounding	Genie maps business questions to actual tables, fields, and relationships. All context comes from your business metadata.	Answers stay rooted in real entities and metrics, not invented ones.
Semantic search	When a user asks a question, Genie retrieves the most relevant tables and fields using semantic understanding, not keyword matching.	Reduces scope hallucination: the model can't invent facts about tables or columns it wasn't shown.
Fact verification	Genie translates questions into SQL, executes it, and returns only values it directly retrieved from your database.	Prevents numeric hallucinations and false relationships. The model can't claim a metric exists if the query returns no results.

How Databricks approaches hallucination prevention

Genie Ontology is the first line of defense. An ontology is a structured representation of your business: which tables exist, what they mean, how they relate to each other, and which fields are metrics vs. dimensions. When a user asks Genie a question, the ontology grounds the model's reasoning. Instead of generating an answer from thin air, Genie retrieves the ontology context relevant to that question.
Semantic search then selects which specific tables and fields are needed. If your database has ten tables about customers, Genie doesn't show the model all of them. It retrieves only the tables semantically similar to the question using embeddings, shrinking the context window and the opportunity for the model to invent.
Finally, Genie translates the question into SQL and runs it. The result is the ground truth. If the query returns no rows, Genie doesn't fabricate an answer; it says the data isn't available. If it returns a value, that's what Genie reports.

Building your ontology to prevent hallucinations

The quality of your ontology directly determines hallucination risk. A sparse, poorly labeled ontology leaves the model guessing about what's real. A rich ontology makes it nearly impossible to hallucinate.

• Name your tables and fields clearly. Use business terms, not database IDs. "customer_lifetime_value" is better than "clv_x42"; "churn_risk_segment" is better than "segment_3".
• Add descriptions. Include what each field represents, its unit of measure (currency, count, percentage), and how it's calculated. The model uses these descriptions as ground truth.
• Link related entities. Specify foreign keys and relationships. If your data has a customer table and an orders table, make that relationship explicit so Genie knows they're connected and doesn't hallucinate cross-table logic.
• Tag sensitive fields. Use Genie's tagging system to mark PII, financial data, or proprietary metrics. This prevents the model from discussing data it shouldn't access.

Monitoring and improving accuracy

Genie includes feedback mechanisms to detect hallucinations in production. When a user flags an answer as wrong or irrelevant, Genie logs it. Over time, these logs show which questions or domains are prone to errors, so you can improve your ontology or add more context.
You can also run accuracy checks: prompt Genie with questions you know the answer to and compare its results to ground truth. If Genie consistently gets financial metrics wrong, it may be a labeling issue in your ontology; if it misunderstands customer segments, you may need to improve relationship definitions.

FAQs

Can Genie hallucinate even with a good ontology?

No system is perfect, but the risk is very low. If Genie has semantic grounding, the right context, and fact verification through SQL, there's almost no surface for hallucination. The remaining edge cases are semantic misunderstandings (the model interprets a question differently than intended) or ontology gaps (the question asks about something that isn't defined). Both are rare with a well-maintained ontology.

What if my data itself is incorrect?

Genie will faithfully report what's in your database. If your source data is wrong, Genie's answer will reflect that. Hallucination prevention is about not inventing new false information; it doesn't fix upstream data quality issues. Use data validation and cleansing before it reaches Genie.

How does Genie handle ambiguous questions?

When a question could map to multiple tables or metrics, Genie asks for clarification rather than guessing. It surfaces the options to the user: "Did you mean revenue by product or revenue by region?" This prevents misinterpretation hallucinations.

Does grounding slow down Genie's responses?

No. Semantic search and ontology lookup are fast. The latency is dominated by query execution against your database, which would happen anyway when retrieving ground truth.

Can I use Genie for domains where I don't have complete data?

Yes, but with caveats. If a question asks about something not in your data, Genie will say so. It won't invent numbers. For open-ended research or context beyond your data, Genie is less useful; it's designed for data-grounded decisions.