This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs).
The book begins by laying the groundwork with the foundations of observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.
In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.
What you will learn:
- How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and
latency analysis.
- Techniques for applying chaos engineering principles to test LLM robustness under stress and
failure scenarios.
- Methods for building SLOs, SLAs, and dashboards tailored to inference quality and model
reliability.
- Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.
Who this book is for:
This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.
Table of Contents:
Part I: Foundations of Observability for LLMs.- Chapter 1: Introduction to LLMs and Observability.- Chapter 2: Site Reliability Engineering (SRE) Overview.- Chapter 3: Observability in AI vs. Traditional Systems.- Part II: Measuring Performance in LLMs.- Chapter 4: Defining Service Level Objectives (SLOs) for LLMs.- Chapter 5: Observability Metrics for LLMs.- Chapter 6: The Role of Logs in LLM Systems.- Chapter 7: Distributed Tracing for LLM Workflows.- Part III: Scaling Observability Across Distributed Systems.- Chapter 8: Observability in Multi-Model Enviroments.- Chapter 9: Capacity Planning and Scaling LLMs.- Chapter 10: Reducing Latency in LLM Systems.- Chapter 11: Fault Tolerant LLM Infrastructure.-Part IV: Chaos Engineering for LLM Reliability.- Chapter 12: Introduction to Chaos Engineering.- Chapter 13: Chaos Experiments for LLMs.- Chapter 14: Automating Chaos Engineering for AI.- Part V: Monitoring and Improving LLM Performance.- Chapter 15: Real-Time Monitoring Systems for LLMs.- Chapter 16: Postmortems for LLM Failure.- Chapter 17: Retraining and Model Drift Monitoring.- Part VI: AI Ethics and Accountability in Observability.- Chapter 18: Governance and Compliance in LLM Systems.- Chapter 19: Telemetry and Accountability.- Chapter 20: Future Trends in AI Observability.
About the Author :
Ankush Sharma is a veteran technologist and AI systems architect with over 20 years of expertise in distributed systems, cloud infrastructure, and AI platform engineering. He has led engineering teams at leading global technology companies and has been an active contributor to open-source AI infrastructure projects. His work has been recognised through conference talks, patents, and leading developer forums. He is based in the Bay Area, US.
