RAG Evaluation Infrastructure
Project Summary
Client: Enterprise Platform Industry: Enterprise Search / Knowledge Management
Impact Metrics:
- Replaced noisy, unreliable quality metrics with calibrated measurements
- Established CI/CD regression testing for search quality
- Reduced evaluation costs using open-source models as judges
- Created a repeatable framework for ongoing quality monitoring
Challenge
The client had a production RAG-based search assistant but no reliable way to measure whether it was actually working well. Existing metrics were noisy and inconsistent — teams couldn't tell if changes improved or degraded search quality. Without trustworthy evaluation, every deployment was a gamble.
Approach
I built a systematic measurement layer designed for production reliability:
- LLM-as-a-judge framework: Calibrated evaluation using well-defined rubrics rather than vague quality scores
- Cost-efficient architecture: Used open-source models as judges instead of expensive commercial APIs, without sacrificing evaluation quality
- CI/CD integration: Automated regression testing so search quality was verified on every deployment
- Metric calibration: Replaced noisy signals with measurements that teams could actually trust and act on
Results
- Trustworthy metrics — teams can now confidently assess whether changes improve search quality
- Automated regression testing — quality is verified in CI/CD, not manually after deployment
- Cost-efficient evaluation — OSS models as judges reduced ongoing evaluation costs
- Repeatable framework — the evaluation infrastructure scales as the search system evolves
Tech Stack
- Python
- LLM-as-a-judge evaluation framework
- Open-source language models for cost-efficient judging
- CI/CD pipeline integration
- Statistical calibration and metric design
My Role
Designed and built the entire evaluation infrastructure — from metric definition and judge calibration to CI/CD integration and production deployment.
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