Book Review: Architecting Generative AI Applications by

I have started reading Architecting Generative AI Applications by Leonid Kuligin, published by Packt — and it tackles one of the most important shifts in AI today: going from a GenAI prototype to a production-ready application.

📖 Book link: Architecting Generative AI Applications

Why This Book Matters

What I like about the book is the practical angle. It is not just about prompts or demos. It covers the engineering reality behind GenAI systems — the gap that most teams struggle with after the initial excitement of a working prototype fades.

This resonates strongly with what I see in enterprise environments: building with LLMs is easy to start, but hard to operationalize well. The real value comes when teams can make GenAI applications reliable, measurable, secure, and scalable — not just impressive in a demo.

Key Topics Covered

The book addresses the full lifecycle of production GenAI systems:

Evaluation of LLM Outputs

One of the hardest problems in production AI. How do you know your model’s responses are actually good? The book covers:

• Automated evaluation frameworks
• Human-in-the-loop assessment
• Regression testing for prompt changes
• Quality metrics beyond simple accuracy

RAG and Agentic Architectures

Retrieval-Augmented Generation has become the standard pattern for grounding LLM outputs in enterprise data. The book goes beyond basic RAG to cover:

• Advanced retrieval strategies (hybrid search, reranking)
• Agentic architectures where LLMs orchestrate multi-step workflows
• Tool use and function calling patterns
• Context architecture decisions that determine agent accuracy

From DevOps and MLOps to LLMOps

This is where platform engineering meets AI. The transition from traditional MLOps to LLMOps introduces new challenges:

• Model versioning is different when you are versioning prompts, not weights
• A/B testing LLM outputs requires different statistical approaches
• Cost management becomes a first-class concern (token economics)
• Latency budgets must account for multi-hop agent chains

For teams already practicing platform engineering, LLMOps extends the internal developer platform with AI-specific capabilities.

Kubernetes, Infrastructure as Code, and Deployment

Production GenAI applications need infrastructure that can:

• Scale inference endpoints based on token throughput
• Manage GPU resources efficiently across teams
• Deploy model updates without downtime
• Handle the memory requirements of large models

The book covers deployment patterns on Kubernetes, which aligns with real-world enterprise infrastructure where most AI workloads land.

Security, Privacy, and Observability

GenAI introduces unique security challenges:

• Prompt injection and OWASP Top 10 for LLMs
• Data leakage through model outputs
• PII handling in RAG pipelines
• Audit trails for AI-generated decisions

Observability for LLM applications goes beyond traditional metrics — you need to trace the reasoning chain, measure hallucination rates, and monitor cost per interaction.

A/B Testing and Online Experiments

Running experiments on LLM outputs is fundamentally different from testing button colors:

• Outputs are non-deterministic
• Quality is subjective and multi-dimensional
• Statistical significance requires different sample sizes
• User satisfaction doesn’t always correlate with output accuracy

Who Should Read This

For engineers, architects, platform teams, and technical leaders working on GenAI applications, this is a very relevant resource. Specifically:

• Platform engineers building internal AI platforms
• ML engineers transitioning from traditional ML to LLM-based systems
• Architects designing production GenAI at enterprise scale
• Engineering managers who need to understand operational complexity

The Production Gap

The book addresses what I call the “demo to production gap” — the 90% of engineering effort that happens after you get a prototype working:

This gap is where most GenAI projects fail. Having a structured reference for navigating it is valuable.

Related Reading

If you are working on production AI systems, these related articles cover specific aspects in depth:

• AI on Kubernetes: The First 90 Days — building your AI platform from scratch
• The Inference Economy — managing inference costs at scale
• NVIDIA NIM Deployment — production model serving
• LLM Quality vs Cost vs Safety — the three-way trade-off
• Context Architecture for AI Agents — from 0% to 92% accuracy

Thanks to Packt, Nimisha Dua, and Leonid Kuligin for sharing this release with the community.

📖 Get the book: Architecting Generative AI Applications