Production-ready evaluation platform for LLM and RAG systems at CERN. Enables objective, repeatable assessment of AI answer quality and context retrieval performance in knowledge-intensive and domain-specific environments.
Business Goal
- Enable trustworthy adoption of LLM-based systems and measurable quality guarantees for AI outputs.
- Support data-privacy-first deployments (including fully local setups).
- Allow stakeholders to compare models, configurations, and architectures using objective metrics.
- Act as a decision-support layer: choose models, tune RAG pipelines, validate AI readiness before production rollout.
Solution
- Answer quality metrics: Correctness (semantic and factual similarity), relevancy to the original question, completeness and coherence.
- Context retrieval effectiveness: Semantic similarity of retrieved documents, contextual recall and precision, URL and source overlap analysis.
- LLM-as-a-Judge evaluation: Automated scoring aligned with human-judgment benchmarks; consistent, explainable scoring pipelines.
- Evaluation dashboard: Model-to-model comparisons, distribution histograms and variance analysis, performance vs. latency insights, domain-specific performance breakdowns.
- Technical: Evaluates answer quality and context retrieval; supports multiple models and configurations; operates entirely locally when required for full data confidentiality.
Results and Impact
- Up to ~30% improvement in answer quality for domain-specific queries when using RAG.
- Reduced hallucination risk through higher relevancy and tighter score distributions.
- Data-sovereign AI deployments without sacrificing quality; repeatable evaluation framework across teams and projects.
- Platform operational for single-prompt and batch evaluations; self-contained local environment with structured logging and persistent storage.