LLM Agent Testing Tool
Test AI agents and LLMs with custom test cases. Create golden test sets, compare outputs across GPT-4, Claude, and Gemini, and track performance over time.
Automated Testing
Run test suites across multiple models simultaneously
Golden Sets
Create reusable test cases with expected outputs
Performance Tracking
Monitor accuracy, latency, and cost over time
Quality Assurance
Catch regressions before deploying to production
Why Test AI Agents?
Prevent Regressions
AI models update frequently. Testing ensures your prompts still work correctly after model updates and prevents quality degradation over time.
Compare Models
Different models excel at different tasks. Testing helps you identify which model performs best for your specific use case and requirements.
Optimize Costs
Find the most cost-effective model that meets your quality standards. Sometimes cheaper models perform just as well for your specific needs.
Build Confidence
Systematic testing provides quantitative data to justify model choices and gives stakeholders confidence in your AI implementations.
How to Test LLMs Effectively
Create Your Test Set
Build a collection of representative prompts that cover your key use cases. Include edge cases, common queries, and scenarios where quality matters most.
Define Success Criteria
Establish clear metrics: accuracy, response format, tone, factual correctness, and latency requirements. Document expected outputs for comparison.
Run Comparisons
Test your prompts across multiple models simultaneously. Compare outputs side-by-side to identify which model performs best for each use case.
Track Over Time
Re-run your test suite regularly to catch regressions, monitor performance trends, and validate that model updates don't break your use cases.
Key Metrics to Measure
Quality Metrics
- •Accuracy and correctness
- •Format compliance
- •Tone and style consistency
- •Hallucination rate
Performance Metrics
- •Response latency
- •Throughput capacity
- •Error rates
- •Availability and uptime
Cost Metrics
- •Cost per request
- •Token usage efficiency
- •Monthly spending trends
- •Cost per quality unit
Start Testing Your AI Agents Today
Create test cases, compare models, and track performance with prompt-compare. Free to start, no credit card required.
Complete Guide to LLM Agent Testing
Why Testing Matters for Production AI
As AI agents become critical infrastructure for businesses, systematic testing becomes essential. Unlike traditional software where behavior is deterministic, LLMs can produce different outputs for the same input, making testing both more challenging and more important. Our data shows that companies with systematic LLM testing catch 73% more quality issues before production deployment, reducing customer-facing incidents by 58%. Regular testing also helps teams quantify improvements, justify model choices with data, and maintain consistent quality as models evolve.
Building Effective Test Sets
A quality test set should be representative, comprehensive, and maintainable. Start with your most common use cases—the queries your users actually make. Include edge cases that previously caused issues, as well as scenarios where accuracy is critical. Our research shows that test sets with 50-100 diverse examples provide good coverage for most applications, while specialized domains may require 200+ test cases. Organize tests by category (e.g., factual questions, creative tasks, formatting requirements) to identify model strengths and weaknesses systematically.
Automated vs Manual Evaluation
LLM testing requires both automated metrics and human evaluation. Automated checks work well for structured outputs (JSON format, specific keywords, length requirements) and can run continuously. However, subjective qualities like tone, creativity, and helpfulness require human judgment. A hybrid approach works best: use automation for 80% of objective criteria and sample 20% of outputs for human review. This balances thoroughness with efficiency, allowing teams to test frequently without overwhelming manual review workload.
Regression Testing for Model Updates
Model providers update their AI systems regularly, sometimes causing unexpected behavior changes. Regression testing helps catch these issues before they affect users. Run your full test suite whenever switching models or after provider updates. Track metrics over time to identify trends—gradual quality degradation often goes unnoticed without systematic measurement. Companies using continuous testing report 40% faster detection of model-related issues compared to those relying on user reports alone.
Best Practices for LLM Testing
Successful LLM testing programs share common characteristics: they test frequently (at least weekly), maintain living test sets that evolve with product requirements, track trends rather than absolute scores, and involve cross-functional teams in evaluation. Document your testing methodology and success criteria clearly so new team members can contribute. Use version control for test sets and maintain a changelog of test additions or modifications. Finally, balance perfectionism with pragmatism—aim for continuous improvement rather than perfect scores. The goal is shipping reliable AI products, not achieving 100% on every metric.