The New Testing Challenge
Your chatbot passed all tests yesterday. Today, it's telling users that 2+2=5 and recommending products you don't sell.
Welcome to the world of LLM testing, where traditional approaches fail and new strategies are essential.
Why Traditional Testing Breaks Down
Deterministic vs Non-Deterministic
Traditional software: Same input → Same output (always)
LLM applications: Same input → Different output (often)
This fundamental difference invalidates most testing assumptions.
The Hallucination Problem
LLMs confidently generate false information. They don't know what they don't know. And they sound authoritative even when completely wrong.
Testing for hallucinations is unlike any testing challenge before.
A Framework That Works
1. Define Quality Dimensions
Before testing, establish what "good" means for your use case:
- **Accuracy**: Is the information factually correct?
- **Relevance**: Does the response address the actual question?
- **Consistency**: Are similar questions answered similarly?
- **Safety**: Does it refuse harmful or inappropriate requests?
- **Tone**: Does it match your brand voice?
- **Completeness**: Does it fully address the user's need?
Each dimension needs specific test strategies.
2. Build a Golden Dataset
Create a curated set of questions with verified answers:
- **Core functionality**: The most important use cases
- **Edge cases**: Unusual but valid inputs
- **Adversarial inputs**: Attempts to trick or manipulate
- **Previously failed cases**: Regression prevention
This dataset becomes your ground truth for evaluation.
3. Use Prompt Variations
Users don't ask questions the same way. Test with variations:
Original: "What's your return policy?"
Variations:
- "How do I return something?"
- "Can I get a refund?"
- "return policy"
- "I want my money back"
- "What if I don't like the product?"
Each variation should produce an acceptable response.
4. Automated Evaluation
Use LLMs to evaluate LLM outputs (yes, really):
- Compare responses against reference answers
- Check for factual consistency
- Detect potential hallucinations
- Score relevance and helpfulness
This scales better than manual review for every response.
5. Human-in-the-Loop Validation
Some things only humans can evaluate:
- Subjective quality assessments
- Brand voice and tone
- Nuanced appropriateness
- Edge case judgment
Build human review into your testing workflow.
Hallucination Detection Strategies
Fact Verification
For factual claims, verify against known sources:
- Cross-reference with your knowledge base
- Check for internal consistency
- Verify citations if provided
Confidence Calibration
LLMs often express high confidence incorrectly. Test whether confidence correlates with accuracy.
Source Attribution
If your LLM cites sources, verify:
- The source actually exists
- The quote is accurate
- The interpretation is correct
Metrics to Track
Response Quality
- Accuracy rate (% factually correct)
- Relevance score (1-5)
- Helpfulness rating (1-5)
Safety
- Hallucination rate (% with false info)
- Safety incident rate (% inappropriate responses)
- Refusal accuracy (correctly refusing bad requests)
User Satisfaction
- Task completion rate
- User ratings
- Escalation rate
Continuous Monitoring
LLM behavior changes over time, even without code changes:
- Model updates affect outputs
- New edge cases emerge
- User behavior evolves
Production monitoring is essential, not optional.
Getting Started
- **Start with high-risk areas**: Where would hallucinations cause the most damage?
- **Build your golden dataset**: Start with 100 critical test cases
- **Implement basic evaluation**: Automated checks for obvious failures
- **Add human review**: For edge cases and quality assessment
- **Monitor production**: Track metrics continuously
LLM testing is a new discipline. The teams that master it will build AI products users can actually trust.