AI Evaluations & Testing
Learn methods and frameworks for evaluating AI agent performance, memory systems, and ensuring reliable behavior in production environments.
AI evaluations are like report cards for AI agents! Just like your teacher gives you tests to see how well you're learning math or reading, we give AI agents tests to see how well they remember things and help people. We check if they remember the right information, if they're being helpful, and if they're being safe. It's like making sure your robot friend is being a good friend and doing what it's supposed to do!
"Writing evals is going to become a core skill for product managers. It is such a critical part of making a good product with AI."
— Kevin Weil, CPO of OpenAI
"If there is one thing we can teach people, it's that writing evals is probably the most important thing."
— Mike Krieger, CPO of Anthropic
Krieger also noted that Anthropic assesses candidates on how they think about AI evals during interviews, adding that "not enough of that talent exists."
As AI agents become more sophisticated and handle critical tasks, rigorous evaluation becomes essential for ensuring reliability, safety, and performance. According to Anthropic's Solutions Architect team, the inability to measure model performance is the biggest blocker of production use cases.
Safety Assurance
Ensure AI agents behave safely and don't cause harm in real-world scenarios.
Performance Optimization
Identify bottlenecks and areas for improvement in agent capabilities.
Trust Building
Provide evidence of reliability for users and stakeholders.
Memory Performance Evaluation
Testing how well agents store, retrieve, and use memory across different scenarios and time periods.
Task Performance Evaluation
Measuring how effectively agents complete specific tasks and achieve desired outcomes.
Safety & Alignment Evaluation
Ensuring agents behave safely, ethically, and in alignment with human values and intentions.
Robustness Evaluation
Testing how agents handle edge cases, adversarial inputs, and unexpected scenarios.
OpenAI Evals
Battle-tested open-source framework for evaluating LLMs with pre-built and custom evaluation tasks.
Anthropic Evals
Official eval framework with code-graded, human-graded, and LLM-graded support. Focus on safety and alignment.
Braintrust
End-to-end LLM evaluation and observability platform with 9+ framework integrations. Enterprise-grade.
LangChain Evaluators
Built-in evaluation tools for LangChain applications with memory and chain testing.
LangWatch / LangSmith / Langfuse
LLM-specific monitoring and evaluation tools with real-time observability and production tracking.
Custom Frameworks
Domain-specific evaluation frameworks built for particular use cases and requirements.
Accuracy Metrics
- • Precision and Recall
- • F1 Score
- • Exact Match
- • BLEU/ROUGE scores
Performance Metrics
- • Response Time
- • Throughput
- • Memory Usage
- • Cost per Query
Quality Metrics
- • Helpfulness Score
- • Coherence Rating
- • Factual Accuracy
- • User Satisfaction
- Use diverse and representative test datasets
- Implement continuous evaluation pipelines
- Combine automated and human evaluation
- Track metrics over time and versions
- Test edge cases and failure modes
- Defining appropriate success metrics
- Handling subjective evaluation criteria
- Scaling evaluation to large datasets
- Avoiding evaluation dataset contamination
- Balancing speed vs thoroughness
Define Success Criteria
Establish clear, measurable criteria for what constitutes successful agent behavior
Create Test Datasets
Develop comprehensive test datasets covering normal use cases, edge cases, and adversarial scenarios
Implement Automated Testing
Set up automated evaluation pipelines that run continuously as your system evolves
Monitor and Iterate
Continuously monitor performance, analyze results, and refine both your system and evaluation methods
Define success criteria for your AI feature - be specific about what "good" means
Create 10-20 test cases covering common scenarios, edge cases, and adversarial inputs
Implement basic code-graded evals for objective criteria (exact match, contains, etc.)
Add LLM-graded evals for subjective quality (helpfulness, tone, clarity)
Run evals on your current system to establish a baseline score
Make a change (prompt tweak, model upgrade) and measure the impact
Add regression tests to prevent breaking existing functionality
Set up CI/CD integration to run evals automatically on every change
Monitor production outputs and add failures to your eval suite
Review failures manually every week to find patterns and improve your system
Pro tip: According to Hamel Husain, spend 60-80% of your time on evals and error analysis. It feels slow at first, but it's the only way to move beyond demos to production-grade AI products.