"LLMs in Production" by Christopher Brousseau and Matthew Sharp is a comprehensive guide for transforming language models into stable, scalable solutions that work in real-world environments. Most materials on LLMs are limited to laboratory examples. But building a real product based on large language models (LLMs) requires completely different approaches: engineering, infrastructure, and business-oriented.
The authors share their experience creating LLM systems — from prototypes to production services. They cover architecture, data processing, security, ethics, API interactions, and solution scaling. This manual is written from the perspective of developers, engineers, and technical leaders who need not just to "play with GPT," but to create reliable LLM-based products that bring real business value.
Download "LLMs in Production" today to understand how to turn an LLM into part of a product, not a laboratory demo. From the beginning of this handbook, you'll start seeing the complete architecture: requests, tokens, UX, infrastructure, logic. This is the perfect guide for ML engineers, backend developers, and those building new LLM-based tools. This training material allows you to stop relying on guesswork and start designing consciously.
Who Should Read This Professional Edition?
This manual is designed for technical specialists working at the intersection of machine learning, development, and product management.
- ML Engineers: Learn how to build pipelines, request processing, logging, and model versioning.
- Backend Developers: Master LLM interaction through APIs, proxying, caching, and load control.
- Technical Product Managers: Gain understanding of architecture and limitations of LLM products when bringing them to market.
- Data Scientists transitioning to development: Learn to create sustainable ML products, not experimental notebooks.
How Does This Guide Differ from Other LLM Publications?
Most books about LLMs explain how to use APIs or build small demos. "LLMs in Production" is a book about sustainability, reliability, and scale. It teaches you to think about language models as part of a complex system that must be fault-tolerant, secure, loggable, and manageable.
The authors break down architectural patterns — from microservices to serverless integrations. Topics covered include latency, throttling, caching, A/B testing, token monitoring, streaming processing, and fallback strategies for LLM failures. A large section is dedicated to developing UX interfaces for LLM interaction and designing user prompts. Methods for protecting against prompt injection, model attacks, and unauthorized data access are also shown.
Special attention is paid to infrastructure: interacting with OpenAI APIs, deploying self-hosted models, cost control, and version management. Examples provide not just code, but architectural schemes, patterns, and anti-patterns that arise in real projects.
This reference contains no "magic solutions," but offers a detailed and pragmatic approach to creating real LLM products — from MVP to production. This makes it especially useful for those who have passed the experimentation phase and want to build something sustainable, scalable, and monetizable.
How Can Knowledge from This Manual Be Applied?
After reading this handbook, you will be able to:
- Deploy architecture for LLM interaction through APIs or local models
- Ensure request security and protection against prompt injection
- Integrate LLMs into backends through queues, caches, and routing
- Set up logging systems, token tracking, and usage metrics
- Implement fallback strategies, limit management, and token budgeting
- Develop business products where LLMs are not toys, but the system core
This guide has earned high praise from developers because examples are taken from real production experience and adapted for various technology stacks.
More About the Author of the Book
The Developer's Opinion About the Book
A practical guide for deploying and scaling large language models in production systems. Topics include inference efficiency, security, memory management, and monitoring. After reading, you’ll know how to ship LLM-based features into products. Recommended for backend developers, ML engineers, and product teams.
Sarah Bennett
Machine Learning Developer
FAQ for "LLMs in Production: From language models to successful products"
1. Does it explain how to use LLMs locally rather than through OpenAI?
Yes, the authors cover both options: SaaS APIs (OpenAI, Anthropic, Cohere) and self-hosted solutions based on LLaMA, Mistral, Falcon. They break down deployment nuances: weight management, containerization, hardware requirements, latency, environment configuration, and load balancing. Trade-offs are also discussed: performance, cost, security.
2. Does "LLMs in Production" include examples of LLM integration with web applications?
Yes, integrations with backends (via REST/gRPC), UI components (chats, forms, editors), and architecture for streaming responses and SSE/WebSocket are described. The authors show how to build secure endpoints, process prompts, cache responses, and log interactions. Special attention is paid to UX principles: what makes LLM interaction convenient, how to combat waiting, how to display tokenized data streams. This makes the material practical for full-stack and backend developers.
3. Will this publication suit me if I don't have an ML background?
Yes, if you're a technical specialist with backend or API integration experience. The authors don't focus on model training, but consider architecture, scaling, and implementation. You'll understand how to communicate with LLMs, pass parameters, work with embeddings, control length and cost. The book suits those implementing ML solutions but not involved in modeling. If you're familiar with REST APIs, queues, databases — the material will be accessible and applicable.
4. Are real cases and production mistakes described?
Yes. The authors share failures and non-standard situations: from OpenAI API overloads to tokenization problems and frontend freezing. Causes and solutions are explained: from adding queues to caching, custom error handling, and UX redesign. This makes the book not an abstract guide, but living experience. These sections are especially valuable: you learn not only how to do things right, but how to avoid critical mistakes that result in user or budget loss.
5. Is the topic of working with Embeddings and Retrieval Augmented Generation (RAG) covered?
Yes. The guide examines building RAG-based systems: creating vector indexes, storage in databases (e.g., FAISS, Weaviate), synchronization with knowledge sources, and LLM integration. Recommendations are given for index updates, relevance control, context limitations. This is especially important when building chats with internal data, documentation search, knowledge bases, and corporate systems. It also discusses how to automate embedding updates without losing quality.
Information
| Author: | Christopher Brousseau, Matthew Sharp | Language: | English |
| Publisher: | Manning | ISBN-13: | 978-1633437203 |
| Publication Date: | February 11, 2025 | ISBN-10: | 1633437205 |
| Print Length: | 456 pages | Category: | Machine Learning and Artificial Intelligence Books |
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