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Observability for LLM Applications: Tracing, Evals, and Shipping AI You Can Trust: 1 - Brossura
Libro 1 di 3: The AI Engineer?s Library
Sinossi
Your LLM feature went out at 2 a.m. last Thursday. Latency is fine. Error rate is zero. And somewhere, quietly, it is lying to a customer.
Traditional observability cannot see this. CPU graphs, HTTP status codes, and p99 dashboards were built for systems that either work or crash. LLMs do neither. They return a confident sentence, the span closes green, and the failure lives in the content.
If you ship LLM features in production — as a backend engineer, a platform engineer, an SRE who inherited someone else's prompt — this book is the operational handbook you have been missing. It is not a theory book about transformers. It is not a prompt engineering tour. It is the stack you actually need on Monday morning to know your AI works.
What you get: the three new pillars (traces, evals, cost and drift metrics), built first on vendor-neutral OpenTelemetry GenAI semantic conventions, then layered with the tools that matter in 2026 — Langfuse, LangSmith, Arize Phoenix, Braintrust, DeepEval, Helicone, and a roll-your-own OTel Collector + ClickHouse + Grafana stack for teams that want everything in-house. Every tool gets an honest verdict: what it is best at, what it is bad at, when to pick it, what it costs.
You will learn how to capture a full LLM decision path as a trace, run evals continuously in CI and in production, track token cost per user and per feature, detect drift before your users do, and write incident response runbooks for a failure mode your pager has never seen. Real code in Python, Go, and TypeScript. Real dashboards. Real traces.
Complementary to Hamel Husain's Evals for AI Engineers (O'Reilly, 2026): where that book goes deep on eval methodology for ML engineers, this one covers the wider operational stack — tracing, tooling, cost, drift, and on-call — for the platform-engineer reader.
By the end, you will have a production-readiness checklist you can run against your own system and mean it when you tell your boss the answer is yes. The first chapter starts with a real incident. Monday morning, you will have something to do.
Book 1 of The AI Engineer's Library.
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Paperback. Condizione: new. Paperback. Your LLM feature went out at 2 a.m. last Thursday. Latency is fine. Error rate is zero. And somewhere, quietly, it is lying to a customer. Traditional observability cannot see this. CPU graphs, HTTP status codes, and p99 dashboards were built for systems that either work or crash. LLMs do neither. They return a confident sentence, the span closes green, and the failure lives in the content. If you ship LLM features in production - as a backend engineer, a platform engineer, an SRE who inherited someone else's prompt - this book is the operational handbook you have been missing. It is not a theory book about transformers. It is not a prompt engineering tour. It is the stack you actually need on Monday morning to know your AI works. What you get: the three new pillars (traces, evals, cost and drift metrics), built first on vendor-neutral OpenTelemetry GenAI semantic conventions, then layered with the tools that matter in 2026 - Langfuse, LangSmith, Arize Phoenix, Braintrust, DeepEval, Helicone, and a roll-your-own OTel Collector + ClickHouse + Grafana stack for teams that want everything in-house. Every tool gets an honest verdict: what it is best at, what it is bad at, when to pick it, what it costs. You will learn how to capture a full LLM decision path as a trace, run evals continuously in CI and in production, track token cost per user and per feature, detect drift before your users do, and write incident response runbooks for a failure mode your pager has never seen. Real code in Python, Go, and TypeScript. Real dashboards. Real traces. Complementary to Hamel Husain's Evals for AI Engineers (O'Reilly, 2026): where that book goes deep on eval methodology for ML engineers, this one covers the wider operational stack - tracing, tooling, cost, drift, and on-call - for the platform-engineer reader. By the end, you will have a production-readiness checklist you can run against your own system and mean it when you tell your boss the answer is yes. The first chapter starts with a real incident. Monday morning, you will have something to do. Book 1 of The AI Engineer's Library. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798257519970
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Paperback. Condizione: new. Paperback. Your LLM feature went out at 2 a.m. last Thursday. Latency is fine. Error rate is zero. And somewhere, quietly, it is lying to a customer. Traditional observability cannot see this. CPU graphs, HTTP status codes, and p99 dashboards were built for systems that either work or crash. LLMs do neither. They return a confident sentence, the span closes green, and the failure lives in the content. If you ship LLM features in production - as a backend engineer, a platform engineer, an SRE who inherited someone else's prompt - this book is the operational handbook you have been missing. It is not a theory book about transformers. It is not a prompt engineering tour. It is the stack you actually need on Monday morning to know your AI works. What you get: the three new pillars (traces, evals, cost and drift metrics), built first on vendor-neutral OpenTelemetry GenAI semantic conventions, then layered with the tools that matter in 2026 - Langfuse, LangSmith, Arize Phoenix, Braintrust, DeepEval, Helicone, and a roll-your-own OTel Collector + ClickHouse + Grafana stack for teams that want everything in-house. Every tool gets an honest verdict: what it is best at, what it is bad at, when to pick it, what it costs. You will learn how to capture a full LLM decision path as a trace, run evals continuously in CI and in production, track token cost per user and per feature, detect drift before your users do, and write incident response runbooks for a failure mode your pager has never seen. Real code in Python, Go, and TypeScript. Real dashboards. Real traces. Complementary to Hamel Husain's Evals for AI Engineers (O'Reilly, 2026): where that book goes deep on eval methodology for ML engineers, this one covers the wider operational stack - tracing, tooling, cost, drift, and on-call - for the platform-engineer reader. By the end, you will have a production-readiness checklist you can run against your own system and mean it when you tell your boss the answer is yes. The first chapter starts with a real incident. Monday morning, you will have something to do. Book 1 of The AI Engineer's Library. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798257519970
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Observability for LLM Applications : Tracing, Evals, and Shipping AI You Can Trust
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Taschenbuch. Condizione: Neu. Neuware - Your LLM feature went out at 2 a.m. last Thursday. Latency is fine. Error rate is zero. And somewhere, quietly, it is lying to a customer. Traditional observability cannot see this. CPU graphs, HTTP status codes, and p99 dashboards were built for systems that either work or crash. LLMs do neither. They return a confident sentence, the span closes green, and the failure lives in the content. If you ship LLM features in production - as a backend engineer, a platform engineer, an SRE who inherited someone else's prompt - this book is the operational handbook you have been missing. It is not a theory book about transformers. It is not a prompt engineering tour. It is the stack you actually need on Monday morning to know your AI works. What you get: the three new pillars (traces, evals, cost and drift metrics), built first on vendor-neutral OpenTelemetry GenAI semantic conventions, then layered with the tools that matter in 2026 - Langfuse, LangSmith, Arize Phoenix, Braintrust, DeepEval, Helicone, and a roll-your-own OTel Collector + ClickHouse + Grafana stack for teams that want everything in-house. Every tool gets an honest verdict: what it is best at, what it is bad at, when to pick it, what it costs. You will learn how to capture a full LLM decision path as a trace, run evals continuously in CI and in production, track token cost per user and per feature, detect drift before your users do, and write incident response runbooks for a failure mode your pager has never seen. Real code in Python, Go, and TypeScript. Real dashboards. Real traces. Complementary to Hamel Husain's Evals for AI Engineers (O'Reilly, 2026): where that book goes deep on eval methodology for ML engineers, this one covers the wider operational stack - tracing, tooling, cost, drift, and on-call - for the platform-engineer reader. By the end, you will have a production-readiness checklist you can run against your own system and mean it when you tell your boss the answer is yes. The first chapter starts with a real incident. Monday morning, you will have something to do. Book 1 of The AI Engineer's Library. Codice articolo 9798257519970
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