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Observability is now a production prerequisite. Tracing vs evaluation, hallucination detection, LangSmith/Langfuse/MLflow, and self-hosting options for KVKK.
What is LLM observability? LLM observability is the practice of tracing every request of a language model application end to end, making prompts, responses, latency, cost, and quality visible. This guide: a clear definition, why it matters, how tracing works, Langfuse and OpenTelemetry, production monitoring metrics, evaluation, KVKK, and FAQs.
The OpenTelemetry GenAI Semantic Conventions standardized LLM tracing. A guide to building an observability pipeline for production LLM systems with token, cost, quality and KVKK balance.
A comprehensive Turkish guide that takes prompt engineering from zero to advanced. Covers the 6 components of a prompt, 14 core techniques (zero-shot, few-shot, CoT, ToT, ReAct, self-consistency, meta-prompting), Turkish-specific notes, 20+ ready templates, model-specific differences (GPT-5, Claude Opus 4.7, Gemini 3), prompt injection defenses, DSPy-based automatic optimization, and A/B testing.