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Ultimate LLMOps with Langfuse gives you the observability, evaluation, and operational discipline to run LLM systems you can actually trust in production, replacing intuition-driven development with measurable, data-driven engineering practice. You begin with LLM monitoring fundamentals, including tracing, drift detection, and bias awareness, then move into Langfuse's core capabilities, covering instrumentation, observability dashboards, prompt management, and structured evaluation. The book addresses automated scoring, human feedback workflows, cost and latency tracking, and production metrics, grounding every concept in concrete examples and real system architectures. The final section delivers end-to-end playbooks for agentic workflows, RAG pipelines, security guardrails, and LLM governance. By the end of the book, you will be able to instrument, evaluate, and operate production LLM applications with full visibility, debug faster, improve quality continuously, and ship AI features with confidence.
Nikhil Talreja is a Senior AI Engineer with over 15 years of experience in Software Development, AI, and People Leadership. He is a math lover and an AI enthusiast experienced in solving complex problems with a background in engineering and a passion for leveraging AI to drive innovation. From Mumbai to Munich, he has built and deployed AI-powered applications that deliver real-world impact, combining technical expertise with a practical understanding of how to run AI at scale.
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