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Book synopsis

Adding an LLM to an application is easy. Building an AI system you can trust in production is much harder.

AI Engineering for Backend Developers is a practical guide for software and backend developers who want to move into modern AI engineering without getting lost in hype, research-heavy theory, or framework-specific tricks.

If you already understand the basics of APIs, databases, authentication, testing, queues, or deployment but are new to LLM applications, RAG, and AI agents, this book gives you a clear path forward.

You will learn how to apply familiar backend-engineering principles to probabilistic systems, design reliable model boundaries, validate outputs, build retrieval pipelines, control tool use, evaluate AI behavior, and operate AI workloads safely in production.

Key Features

• Production-focused guidance for LLM applications and AI agents

• Clear explanations of RAG, context engineering, structured outputs, and tool calling

• Practical coverage of testing, evaluation, observability, security, scalability, and cost

• Provider-neutral architecture patterns

• Practical exercises and production-readiness checklists

• Step-by-step progression for developers new to AI engineering

What You Will Learn

• Integrate LLM APIs through controlled backend boundaries

• Validate structured model outputs safely

• Build effective context and RAG pipelines

• Use embeddings, vector search, hybrid retrieval, and reranking

• Design secure tool-calling workflows

• Engineer bounded AI agents with execution limits

• Test and evaluate LLM, RAG, and agent behavior

• Improve observability, security, reliability, and cost control

• Deploy and operate AI backends with safer release practices

Who This Book Is For

Backend developers, software engineers, application developers, technical students, and self-learners who already understand basic software-development concepts but are new to production AI engineering.

No previous experience with LLM APIs, vector databases, RAG systems, AI agents, or evaluation frameworks is required.

Table of Contents

1. Engineering AI Systems as Production Backend Software

2. Production LLM Integration and Structured Outputs

3. Context Engineering for LLM Applications

4. Retrieval-Augmented Generation in Production

5. Tool Calling and Controlled AI Workflows

6. Engineering Bounded AI Agents

7. Testing, Evaluation, and Reliability Engineering

8. Observability, Security, and Production Trust Boundaries

9. Performance, Scalability, and Cost-Aware Architecture

10. Deploying and Operating Production AI Backends

Move beyond impressive AI demos and start building LLM applications and AI agents with the discipline expected from serious production software.

Start building reliable AI systems today.

Book details

• Full title: AI Engineering for Backend Developers
• Subtitle: A Practical Guide to Building Reliable LLM Applications and AI Agent Systems for Production
• Author: Adrian Solvek
• Language: English
• Binding: Paperback
• Number of pages: 266
• EAN: 9798170851911
• ID: 53696208
• Publisher: Independently published
• Weight: 468 g
• Dimensions: 254 × 178 × 14 mm
• Date of publishing: 30. August 2026

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