How to reduce hallucinations when using LLMs for data analysis tasks?

Question

I'm building a system where users ask questions about their data in natural language, and an LLM generates SQL queries and interprets results.

**The problem:** The LLM often "hallucinates" insights that aren't supported by the actual data.

**Example:**
- User asks: "What's our top-selling product?"
- LLM correctly generates SQL
- But then adds: "This is likely due to the recent marketing campaign" (we had no such campaign)

**What I've tried:**
1. Strict system prompts saying "only state facts from the data"
2. Few-shot examples of good vs bad responses
3. Temperature = 0 for deterministic output

Still getting hallucinations. How do production data analysis tools handle this? Should I use a separate verification step?

Dr. Sarah Chen · Accepted Answer

Hallucinations in data analysis are particularly dangerous. Here's a multi-layer approach:

**Layer 1: Constrained Generation**
Force the model to cite data:
```
System: You are a data analyst. For every claim, cite the specific data point.
Format: [CLAIM] (Source: [TABLE.COLUMN])

Example:
"Revenue increased 23% (Source: sales.monthly_revenue)"
```

**Layer 2: Verification Step**
Add a separate verification agent:
```python
analysis = analyst_llm.generate(query, data)
verification = verifier_llm.check(analysis, data)
if verification.has_unsupported_claims:
    analysis = remove_unsupported_claims(analysis)
```

**Layer 3: Confidence Scores**
Ask the model to rate confidence:
```
For each insight, provide:
1. The insight
2. Supporting data
3. Confidence (0-100%)
```

Filter out low-confidence claims.

**Layer 4: Human-in-the-Loop**
For production, show:
- ✅ Facts directly from data (green)
- ⚠️ Inferences/interpretations (yellow, with "AI interpretation" label)
- ❌ Unverified claims (filtered out)

This approach reduced our hallucination rate from ~30% to <5%.

How to reduce hallucinations when using LLMs for data analysis tasks?

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