Ultimate LLMOps with Langfuse

Master Langfuse and Build LLM Systems That Perform in Production

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Covers Production LLM observability like traces, costs, latency, and drift detection.
● Structured prompt management with versioning, testing, and safe deployment workflows.
● Continuous LLM evaluation using automated scoring, feedback, and regression testing.

Book Description
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.

What you will learn
● Instrument LLM applications with end-to-end tracing and observability pipelines.
● Detects model drift, bias, and quality regressions in production systems.
● Manage, version, and deploy prompts across production AI applications.
● Evaluate LLM outputs using automated scoring and human feedback workflows.
● Build dashboards tracking cost, latency, safety, and production performance.
● Apply guardrails and governance frameworks for secure LLM deployments.

1. Introduction to Large Language Models and Monitoring
2. LLM Monitoring Principles
3. Detecting Model Drift and Bias in LLMs
4. Introduction to Langfuse
5. Observability in Langfuse
6. Prompt Management in Langfuse
7. Evaluating LLMs in Langfuse
8. Deriving Actionable Insights Using Langfuse Metrics
9. Administration, LLM Security, and Guardrails
10. Langfuse Best Practices
11. Langfuse Playbooks
12. Putting It All Together
Index

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.

About the Technical Reviewer
Sharat Priya is a Senior Manager in the Wealth and Asset Management Technology practice at Ernst & Young (EY) United States, bringing over 22 years of progressive, hands-on experience designing and delivering complex financial platforms for global banks, custodians, insurers, and fintech firms. His career spans both industry and consulting, marked by technical leadership, architectural innovation, and client-centric product delivery.

Sharat’s work sits at the intersection of technology strategy and product architecture, with a focus on building real-world platforms that modernize financial services. Currently, he advises executive leadership teams on platform modernization, AI adoption, and operating model transformation across the U.S. wealth management landscape.

Sharat also contributes as an industry reviewer and awards judge, and regularly mentors product leaders and architects. His work emphasizes building explainable, resilient, and compliant financial systems that operate at scale, where trust, precision, and impact matter most.

Archana Choudhary is a senior project and transformation leader with extensive experience driving large-scale technology, data, and AI-enabled delivery across global enterprises. She has led complex initiatives spanning infrastructure, cloud, cybersecurity, and enterprise platforms, focusing on governance, execution excellence, and value realization.

With a background as a Global PMO Lead, Agile Transformation Leader, and Program Manager, Archana has worked at the intersection of technology delivery, operating models, and leadership, helping organizations move from traditional execution approaches to data-driven, adaptive ways of working. Her work increasingly focuses on how AI, automation, and analytics reshape project visibility, decision-making, and risk management at scale.

Through her research and practical application of AI-enabled delivery and LLM observability, including tools such as Langfuse, Archana explores how organizations can bring transparency, reliability, and governance into GenAI-powered systems ensuring that AI augments human judgment, rather than obscuring it. She is particularly interested in how monitoring, traceability, and feedback loops enable responsible AI adoption in enterprise environments.