RAG EVALUATION
Observability & quality assurance for production AI
Build evaluation infrastructure that catches regressions before users do. RAGAS metrics, LangSmith traces, and automated quality gates for production RAG systems.
Key Features
RAGAS baseline benchmarks (precision, recall, faithfulness)
LangSmith trace observability dashboard
Regression test suite with CI/CD integration
Hallucination detection and cost monitoring
How We Work Together
A proven methodology that delivers results
Discovery
We start with understanding your business, challenges, and goals through workshops and interviews.
Design
Together we design the solution architecture and create a detailed implementation plan.
Deliver
Iterative implementation with regular demos and feedback loops to ensure alignment.
Support
Post-launch support, knowledge transfer, and ongoing optimization recommendations.
Use Cases
- Baseline quality benchmarking for RAG systems
- Automated regression testing pipeline
- Hallucination detection and monitoring
- LLM cost tracking and optimization
Ideal For
- Teams with RAG in production
- AI teams needing quality assurance
- Regulated industries requiring auditability
Not Ideal For
- Prototype-only projects not going to production
- No budget for evaluation infrastructure
- Single-query systems without quality needs
Deliverables
Deliverables
-
01RAGAS evaluation baseline report
-
02LangSmith observability setup
-
03Automated regression test suite
-
04Quality monitoring dashboard
Technology Stack
Timeline
3-4 weeks
Estimated project duration
Related Case Studies
RAG Document Processing System
At Insly, I led development of a RAG (Retrieval-Augmented Generation) system that gives insurance brokers fast, context-aware answers about policy details. The system combines traditional search with vector embeddings to handle complex queries across 23 different insurance providers.
Challenge
Insurance brokers needed to quickly find relevant information across thousands of policy documents from 23 different insurers, each with unique formats and terminology.
Microservices Migration
CloudAcademy needed to migrate their content authorization service from Kotlin to Go as part of a broader standardization effort. I led this migration while ensuring zero downtime and creating new microservices following DDD patterns.
Challenge
Legacy Kotlin service had performance bottlenecks and was difficult to maintain. Team needed to standardize on Go for better consistency across microservices.
Fleet Analytics & Driver Planning Platform
I built a fleet analytics platform for a logistics company managing 300+ trucks and 400+ drivers. The system aggregates data from multiple internal sources—scheduling system (Navigator), HR database, and vehicle registry—to provide unified reporting on driver-vehicle balance, anomaly detection, and operational metrics.
Challenge
Operations data scattered across scheduling system, HR database, and vehicle registry with no unified view of driver availability vs fleet capacity.
Ready to Transform Your Business?
Let's discuss how I can help you achieve your goals. The first consultation is free.