🚀 Building the Enterprise-Grade AI Evaluation Platform the Industry Needs

# The dream: Evaluate any AI model with 3 lines of code
from novaeval import Evaluator
evaluator = Evaluator.from_config("evaluation.yaml")
results = evaluator.run()

The Technical Challenge

As AI models proliferate, developers face a critical problem: How do you systematically compare GPT-4 vs Claude vs Bedrock for your specific use case?

Most teams resort to manual testing or build custom evaluation scripts that break every time APIs change. We needed something better.

Enter NovaEval

NovaEval is an open source, enterprise-grade evaluation framework that standardizes AI model comparison across providers.

Technical Architecture:

• Unified Model Interface: Abstract away provider differences
• Pluggable Scorers: Accuracy, semantic similarity, custom metrics
• Dataset Integration: MMLU, HuggingFace, custom datasets
• Production Ready: Docker, Kubernetes, CI/CD integration

Code Example:

# evaluation.yaml
dataset:
  type: "mmlu"
  subset: "abstract_algebra"
  num_samples: 500

models:
  - type: "openai"
    model_name: "gpt-4"
  - type: "anthropic"
    model_name: "claude-3-opus"

scorers:
  - type: "accuracy"
  - type: "semantic_similarity"

CLI Power:

# Quick evaluation
novaeval quick -d mmlu -m gpt-4 -s accuracy

# Production evaluation
novaeval run production-config.yaml

# List available options
novaeval list-models

Contribution Opportunities

We're actively seeking contributors in:

🧪 Testing: Improve our 62% test coverage
📊 Metrics: Build RAG and agent evaluation frameworks
🔧 Integrations: Add new model providers and datasets
📚 Documentation: Create tutorials and examples

Getting Started:

1. pip install novaeval
2. Check out: https://github.com/Noveum/NovaEval
3. Look for good first issue labels
4. Join our GitHub Discussions

Discussion Questions:

• What evaluation metrics matter most for your AI applications?
• Which model providers would you like to see supported?
• What's your current AI evaluation workflow?