7 posts
The embedding model choice determines the fate of Turkish RAG quality. Multilingual vs Turkish-specific, dimension and performance, reading benchmarks, and building your own evaluation.
The August 2026 frontier landscape: neck-and-neck on SWE-bench Verified, separation on SWE-bench Pro. Which model for which job, benchmark literacy, and the Turkish-performance criterion.
Do you really need a reranker? When reranking adds value and when it is unnecessary in a RAG retrieval pipeline, cross-encoders, benchmarking, and a decision guide.
What is LLM evaluation? A comprehensive enterprise guide to eval metrics, benchmark and test set design, LLM-as-judge, calibration, RAG evaluation, and production monitoring.
Vector database comparison: we evaluate Qdrant, Milvus, Weaviate, and pgvector for enterprise RAG in terms of scale, performance, cost, data sovereignty, and benchmarking.
Frontier models as of July 2026: benchmarks, price/performance and an enterprise selection guide. Which model for which job? Practical field notes.
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.