I built IAnversic EVAL, a platform focused on evaluating and improving the reliability of AI applications, particularly LLM-powered systems and Retrieval-Augmented Generation (RAG) pipelines.

The project addresses a key challenge in AI engineering: moving beyond subjective assessments of model responses toward systematic, repeatable evaluation. It is designed to help developers assess answer quality, factual grounding, and retrieval performance, identify failure cases, and validate changes before deploying them.

Project outcomes and capabilities:

Designed a dedicated platform for evaluating LLM applications and RAG workflows.

Focused on assessing response quality and the relationship between generated answers and retrieved context.

Established a foundation for repeatable evaluations to compare configurations and identify regressions.

Connected evaluation to practical AI engineering workflows, supporting evidence-based decisions about prompts, retrieval strategies, and model selection.

IAnversic EVAL is part of the broader IAnversic ecosystem, complementing IAnversic RAG with an evaluation-focused approach to building more reliable, measurable, and production-ready AI systems.