LLM Evaluation & Optimization
DeepEval metrics, DSPy prompt optimization, and automated test synthesis. Build reliable AI applications with comprehensive evaluation.
The LLM Quality Challenge
Building production-ready LLM applications requires rigorous testing and evaluation. Without proper metrics and testing infrastructure, teams struggle to measure quality, detect regressions, and optimize performance.
- Measuring LLM output quality consistently and objectively
- Optimizing prompts without manual trial and error
- Creating comprehensive test suites for AI applications
- Integrating LLM testing into development workflows
Meet Knova-Forge
The complete LLM evaluation and optimization platform
Define
Define your evaluation criteria and test scenarios using our intuitive interface.
Evaluate
Run comprehensive evaluations across multiple LLM configurations.
Optimize
Use DSPy to automatically optimize prompts based on evaluation results.
Powerful Capabilities
Everything you need for LLM quality assurance
DeepEval Integration
Comprehensive metrics including faithfulness, relevance, coherence, and factual consistency
DSPy Optimization
Automated prompt optimization using DSPy's declarative approach
Test Synthesis
Automatically generate test cases from your documents and use cases
CI/CD Integration
Native integration with GitHub Actions, GitLab CI, and other pipelines
Regression Testing
Detect performance regressions before they reach production
Custom Metrics
Define domain-specific evaluation criteria for your use case
DeepEval Metrics
Industry-standard evaluation metrics for RAG and LLM applications
Faithfulness - Does the output align with source context?
Answer Relevancy - Is the response relevant to the query?
Contextual Precision - Are retrieved contexts properly ranked?
Contextual Recall - Are all relevant contexts retrieved?
Hallucination Detection - Does output contain fabricated facts?
Coherence - Is the response logically structured?
Automated Prompt Optimization
Knova-Forge integrates DSPy's declarative approach to prompt engineering, enabling automated optimization based on your evaluation metrics.
Declarative Signatures
Define what you want, not how to prompt it
Automatic Optimization
Prompts are optimized based on evaluation results
Version Control
Track prompt versions and their performance
A/B Testing
Compare prompt variations with statistical rigor
CI/CD Integrations
Embed LLM evaluation into your development workflow
GitHub Actions
Automated LLM testing on every pull request with detailed reports
GitLab CI
Native GitLab pipeline integration with merge request comments
Jenkins
Plugin for Jenkins-based CI/CD with build quality gates
API Access
REST and Python SDK for custom integration scenarios
Real-Time Dashboards
Monitor evaluation metrics and trends in real-time
Enterprise Security
SOC 2 compliant with secure data handling
Flexible Deployment
Cloud, on-premise, or hybrid deployment options
Automated Test Synthesis
Knova-Forge automatically generates comprehensive test suites from your documents, use cases, and production logs.
Document-Based Tests
Generate Q&A pairs from your knowledge base
Edge Case Generation
Automatically discover edge cases and adversarial inputs
Production Log Analysis
Create tests from real user interactions
Continuous Expansion
Test suite grows automatically over time
The Knova-Forge Advantage
Build reliable AI applications with confidence
Test Coverage
Automated test synthesis maximizes coverage
Faster Optimization
DSPy automation vs manual prompt tuning
Undetected Regressions
CI/CD integration catches issues early
Build Reliable AI Applications
Start evaluating and optimizing your LLM applications with Knova-Forge