Generative AI testing tools
What are generative AI testing tools?
These are frameworks, platforms, or methodologies designed to:
- Run structured tests on LLM prompts or chains
- Score and compare outputs
- Monitor performance in pre-production and production
Generative AI systems differ from traditional ML because their outputs are open-ended and difficult to evaluate with standard metrics. Testing tools bring structure and automation to this challenge.
Why they matter in AI/ML
Without proper testing, generative AI can:
- Produce hallucinated or toxic responses
- Fail to follow prompt instructions
- Generate biased or unsafe outputs
- Cause cost overruns due to inefficient prompting
Testing tools:
- Catch issues early in development
- Help teams iterate faster
- Ensure safety and quality for end users
Common capabilities in GenAI testing platforms
- Prompt evaluation across variants
- LLM-as-a-judge scoring
- Custom rubrics for brand alignment and tone
- Error tagging (e.g., hallucination, refusal, formatting errors)
- Regression testing for prompt or model version changes
- Cost and latency tracking
- Traces for multi-step agents or chains
Examples of generative AI testing tools
- Openlayer – Test and monitor LLMs across prompts, agents, and use cases
- Helicone – Observability for LLM performance
- PromptLayer – Version and monitor prompt performance
- Traceloop – Debug and trace LangChain apps
- Custom Notebooks/Frameworks – Built in-house using APIs and logging tools
Related
Explore related entries to understand how leading teams test, compare, and validate generative AI before it ships.