Mohanna / Kar / Ralte Practical LLM Evaluation for Production Systems

Measure, monitor, and improve AI system reliability across training and inference
1. Auflage 2026
ISBN: 978-1-80742-388-9
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 488 Seiten

Modern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks.
Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives.
Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production.
By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence.
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Autoren/Hrsg.

Mohanna, Ammar

Kar, Indrajit

Ralte, Zonunfeli

Leseproben

Preface

LLMs have transformed AI from a research discipline into an engineering discipline. What began as systems capable of generating remarkably fluent text has rapidly evolved into models that retrieve information, reason through complex problems, interpret images and documents, call external tools, interact with software, and power production applications across virtually every industry.

Building these systems, however, is only half the challenge. The greater challenge is knowing whether they actually work.

Traditional software engineering relies on deterministic testing, where the same input reliably produces the same output. Modern AI systems operate differently. Their behavior is probabilistic, influenced by data quality, prompts, retrieval pipelines, decoding strategies, tool execution, model updates, and runtime conditions. A system may perform flawlessly during demonstrations yet fail unexpectedly in production because of distribution shift, retrieval failures, hallucinations, routing errors, grounding failures, safety violations, or changing user behavior.

As AI systems become more capable, evaluation becomes more important—not less.

This book treats evaluation as a continuous engineering discipline rather than a collection of benchmark scores or offline experiments. Instead of viewing evaluation as something performed after a model has been built, we present it as an operational capability that spans the entire AI lifecycle, from data preparation and model training through inference, deployment, monitoring, and production operations.

Throughout the book, we move beyond evaluating standalone language models and instead focus on evaluating complete AI systems. Modern production applications combine prompts, retrieval pipelines, external tools, structured outputs, safety policies, runtime controls, orchestration frameworks, and monitoring infrastructure. Reliability therefore depends on the interaction of all these components rather than on model quality alone.

To make these concepts concrete, the book follows a progression through increasingly sophisticated AI systems. We begin with text-only LLMs, establishing the core evaluation principles that apply across training and inference. From there, we extend these ideas to vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, computer-using agents, information extraction and document-understanding systems, and specialized production models such as Text2SQL, Text2Cypher, guardrail models, and embedding models. Each chapter introduces the unique failure modes, evaluation strategies, operational metrics, and deployment controls required for that class of systems.

A recurring theme throughout the book is that every meaningful metric should lead to an engineering decision. Evaluation is valuable only when it informs action, whether that action is retraining a model, rejecting a deployment candidate, introducing runtime safeguards, rolling back a release, or escalating a request for human review. Metrics without operational consequences are merely dashboards; production evaluation requires thresholds, gates, and predefined responses.

The goal of this book is not to advocate a particular framework, vendor, or model family. Instead, it provides principles and engineering patterns that remain applicable as the AI ecosystem continues to evolve. Models will improve, architectures will change, and new capabilities will emerge, but the need for systematic, measurable, and production-oriented evaluation will remain constant.

Whether you are an AI engineer building production applications, an ML engineer responsible for deployment pipelines, a platform architect designing enterprise AI systems, or a technical leader establishing evaluation standards within your organization, this book aims to provide practical guidance for building AI systems that are not only capable, but also reliable, measurable, and trustworthy.

Ultimately, successful AI systems are not defined by how impressive they appear in demonstrations. They are defined by how consistently they behave under real-world conditions. Evaluation is what bridges that gap, transforming powerful models into dependable production systems.

Who this book is for

This book is written for AI engineers, machine learning engineers, data scientists, MLOps and LLMOps practitioners, software architects, and technical leaders who are building, deploying, or evaluating production-grade LLM and SLM systems. It assumes a basic understanding of LLMs and focuses on helping readers move beyond prompt engineering and benchmark scores toward designing reliable, measurable, and trustworthy AI systems.

Whether you are developing chatbots, RAG systems, AI agents, reasoning models, multimodal applications, document-understanding pipelines, or specialized enterprise AI solutions, this book provides a practical framework for evaluating their behavior across the entire lifecycle. Rather than focusing on a specific model or evaluation framework, it presents reusable principles and methodologies that can be applied across a wide range of architectures and evolving AI technologies.

What this book covers

, , establishes the foundations of LLM evaluation by introducing a practical, production-oriented framework for assessing LLM systems. It explains why evaluation should be treated as a decision-making process rather than a collection of metrics, introduces the core building blocks of modern evaluation pipelines, and covers evaluators, test suites, metrics, thresholds, monitoring, and deployment gates that underpin reliable LLM applications.

, , focuses on evaluating text-only language models during training, before they reach production. It explores data quality, coverage, contamination, instruction and preference tuning, safety alignment, robustness, and release readiness, showing how disciplined evaluation throughout the training pipeline leads to more reliable, safer, and more generalizable models.

, , shifts from model development to live deployment, presenting inference-time evaluation as a production control system. It covers input validation, prompt governance, output contracts, retrieval and tool evaluation, runtime safety, monitoring, performance, and rollout strategies, demonstrating how to continuously enforce correctness, safety, reliability, and cost constraints in production systems.

, , extends the evaluation framework to vision language models, where grounding between visual and textual information becomes the central challenge. It presents methods for constructing grounding-aware evaluation datasets, measuring hallucinations, robustness, alignment, and safety during training, and introduces production-oriented evaluation gates that ensure multimodal models are ready for deployment.

, , completes the transition to production by introducing inference-time evaluation for vision language models. It covers image intake, visual preprocessing, grounded output validation, evidence-bound claims, runtime safety, monitoring, drift detection, performance management, and deployment strategies, providing a comprehensive framework for operating reliable multimodal AI systems in real-world environments.

, , extends the LLM evaluation framework to multimodal conversational LLMs that integrate voice, image, and text. It shows how to evaluate these models during training by measuring speech text alignment, cross-modality fusion accuracy, and robustness to audio and visual bias; it also shows how to evaluate these models at inference time by assessing transcription drift, reasoning degradation, latency, and cost. The chapter then demonstrates how these evaluation signals roll up into business level metrics such as conversational success rate, user friction, and fallback effectiveness, enabling practitioners to systematically evaluate and improve multimodal conversational LLM powered systems rather than treating them as opaque end to end pipelines.

, , focuses on evaluating mixture of experts (MoE) LLMs by treating expert routing as a first-class evaluation problem across training and inference. It teaches readers how to measure expert routing accuracy, load balance, and expert collapse during training, and how to evaluate routing stability, cold expert activation, latency variance, and output consistency at inference time. The chapter connects these model-level and runtime evaluation signals to system-level reliability and cost efficiency, enabling practitioners to detect and correct MoE-specific failure modes before they impact real use cases.

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