Knowledge Retrieval Evaluation Engineer

Building a knowledge base is only half the challenge — knowing whether it actually retrieves the right information when your AI system needs it is equally critical and far more often neglected. Poor retrieval quality is the root cause of most AI answer failures, hallucinations, and user trust breakdowns in production knowledge systems. This AI assistant specializes in designing and implementing retrieval evaluation frameworks that give you precise, measurable insight into how well your knowledge base is performing.

The assistant helps you define what good retrieval looks like for your specific use case — because the right evaluation criteria depend on your query types, answer requirements, and user expectations. It designs evaluation datasets: sets of representative queries with ground-truth relevant documents or chunks against which retrieval outputs can be scored. It advises on both human-labeled evaluation sets for accuracy and synthetic evaluation generation techniques that scale.

With evaluation datasets in hand, the assistant designs a retrieval metrics framework covering precision, recall, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), context relevance, and faithfulness — explaining what each metric measures and which combination is most diagnostic for your use case. It helps you run structured evaluations, interpret results, and identify the specific failure modes causing quality degradation: poor chunk boundaries, embedding model misalignment, metadata filtering errors, query-document semantic mismatch, or knowledge gaps.

The assistant also designs continuous evaluation infrastructure: automated regression testing pipelines that alert you when knowledge base changes or model updates degrade retrieval quality, A/B testing frameworks for comparing retrieval configurations, and dashboards for tracking retrieval KPIs over time. It bridges the gap between knowledge base construction and AI system quality assurance.

This tool is ideal for AI engineers tuning RAG systems, teams preparing a knowledge base for production deployment, product managers who need retrieval quality metrics for stakeholder reporting, and anyone troubleshooting unexpectedly poor AI answer quality in a deployed knowledge system.