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Ultimate LLMOps with Langfuse Instrument, Evaluate, and Operate Production-Grade LLM Applications with Langfuse [#1031758]
Ultimate LLMOps with Langfuse: Instrument, Evaluate, and Operate Production- Grade LLM Applications with Langfuse
by Nikhil Talreja
English | 2026 | ISBN: 9349887541 | 347 pages | True EPUB | 8.06 MB
Master Langfuse and Build LLM Systems That Perform in Production
Key Features
● Get a free one-month digital subscription towww.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.
Table of Contents
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
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