8 posts
The four core metrics for measuring RAG systems: faithfulness, answer relevancy, context precision and recall. Evaluation with RAGAS, thresholds and context trust.
Building RAG is easy, proving it reliable is hard. Retrieval/generation metrics, reference-free evaluation with RAGAS, OpenTelemetry spans, and cost-per-successful-output.
2026 RAG is no longer linear. Adaptive routing, the agentic retrieve-reason-retrieve loop, five production patterns, and the highest-ROI intervention: hybrid retrieval + reranker.
Measure your RAG system's real quality with four core metrics: faithfulness, answer relevance, context precision, and recall. Ragas, LLM-as-a-judge, and Turkish challenges.
What is LLM evaluation? LLM evaluation (eval) is the systematic measurement of a large language model's or LLM-based application's outputs for accuracy, consistency and safety. This guide: a clear definition, why it matters, evaluation metrics, LLM as a judge, benchmarks, ragas, offline vs online eval, KVKK, and FAQs.
LLM-as-a-judge is the dominant automated evaluation method but biased; RAGAS human correlation is only 0.55. A reliable eval guide covering best practices, biases, in a Turkish/KVKK context.
'Seems to work' is the costliest sentence in AI. Moving to measurable quality with eval sets, LLM-as-judge, RAGAS, and retrieval metrics — including Turkish eval.
A comprehensive reference for designing, scaling, and shipping Retrieval-Augmented Generation (RAG) systems in production with KVKK compliance. Covers Turkish-capable embedding model selection, vector DB comparison, chunking, hybrid search, re-ranking, hallucination control, eval harness, and three anonymized Turkish enterprise case studies — end-to-end production architecture.