Building Production-Ready Gen AI Systems

Eight Patterns, Twelve Principles & a 34-Point Checklist for LLM Systems in Production —

Most Gen AI systems don't fail because of the model. They fail because of what's built around it.

Building Production-Ready Gen AI Systems gives you the complete architectural framework: a four-level maturity model to scope the right ambition, a six-layer canonical architecture, eight named design patterns from Naive RAG to Multi-Agent Collaboration, twelve non-negotiable principles distilled from production failures, and a 34-point engagement checklist that treats evidence as the only acceptable proof of readiness.

This is not a tutorial. It is a practitioner's reference for engineers and architects who need to build LLM systems that survive production — observable, auditable, and safe to change.

Srinivas Bommena

Most Gen AI systems don't fail because of the model. They fail because of what's built around it.

This is not a tutorial. It is a practitioner's reference for engineers and architects who need to build LLM systems that survive production — observable, auditable, and safe to change.

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About the Book

Based on what's actually in the book, here's what I'd write for the Leanpub "About this Book" section:

Most teams building Gen AI systems fail not because they chose the wrong model, but because they built the wrong architecture around it. Prompts hardcoded in application code. No evaluation harness. No gateway layer. No plan for what happens when the model provider goes down or silently changes behaviour. These are not edge cases — they are the default failure mode of every LLM system built without a production-first architecture.

This book exists to prevent those failures.

What this book covers

Building Production-Ready Gen AI Systems is a practitioner's reference for architects, senior engineers, and technical leads building LLM-based applications in production. It covers the full journey from assessing organisational readiness to deploying a system that is observable, auditable, and safe to change:

The AI Maturity Model — Four progressive levels (L1 Foundational through L4 Adaptive) that help you scope the right architectural ambition before committing to a design. Includes a pre-engagement assessment framework and capability coverage table.
Canonical Architecture — A six-layer reference blueprint (Clients, Gateway, Orchestration, Models, Data, Eval & Ops) covering every component a production LLM system requires, from the AI Gateway that makes costs auditable to the feedback loop that turns user corrections into ground truth.
Eight Design Patterns — Named, reusable patterns from Naive RAG to Multi-Agent Collaboration, each with When to Use criteria, Strengths and Limitations tables, and explicit trade-off analysis. Two patterns are documented with rejection rationale — showing when not to use a pattern is as important as showing when to use one.
Twelve Architecture Principles — Non-negotiable standards distilled from production failures, including Version Everything, Guardrails Are Not Optional, Own the Embedding Contract, and Isolate Tenant Data. Each principle includes a concrete example and an anti-pattern showing what goes wrong when the principle is ignored.
The 34-Point Engagement Checklist — A sequenced quality gate covering pre-engagement through production go-live, organised into three phases: Pre-Engagement (6 items), Architecture & Design (8 items), and Delivery & Handover (20 items). Every item requires evidence, not acknowledgement.

A complete worked case study applies every section of the framework to an autonomous M&A due diligence agent — a complex, regulated, multi-agent system that illustrates real architectural decisions including two patterns that were explicitly considered and rejected.

Who this book is for

This book is written for engineers and architects who are comfortable with distributed systems, REST APIs, and cloud infrastructure, but are newer to building LLM systems in production. It is also the right reference for technical leads who need a shared language and a structured framework for governing AI projects across teams.

It is explicitly scoped to LLM-based Gen AI systems — RAG pipelines, agentic workflows, and structured output systems built on foundation models. It does not cover computer vision, recommendation systems, or time-series forecasting.

What this book is not

This is not a beginner's guide to large language models. It does not teach you how to write prompts or choose between GPT-4o and Claude. It assumes you have already decided to build with LLMs and need to know how to build systems around them that survive production.

It is also not a textbook. The patterns and principles are synthesised from production deployments across financial services, healthcare, logistics, and public sector — they represent what has consistently worked and what has consistently failed, presented as a practitioner's reference rather than a statistically validated study.

Why this book now

The gap between "it works in a demo" and "it works in production" has never been larger or more expensive. Teams are reaching for multi-agent orchestration when a simple retrieval pipeline would solve the problem. Or they are building pipelines so primitive they cannot survive contact with real users. The maturity model, patterns, principles, and checklist in this book are the framework that closes that gap.

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About the Author

Srinivas Bommena

Srinivas is a Generative AI Practitioner and Educator specializing in the architectural design and rigorous evaluation of LLM-powered applications. With deep experience in developing multi-agent frameworks and hybrid RAG architectures, he focus on bridging the gap between experimental AI and production-ready systems.

He is the creator of popular technical practice tests on Udemy, including the AWS Certified GenAI Developer - Professional series, and have developed comprehensive frameworks for AI project estimation and compliance. His work frequently involves industry-leading evaluation tools such as RAGAS, Giskard, and Guardrails.ai.

Driven by the mission to help IT professionals navigate the "mindset shift" required for the AI era, Srinivas provides systematic, data-driven methodologies for building AI that is not only innovative but reliable and compliant with emerging standards like the EU AI Act.

Table of Contents

Table of Contents — Building Production-Ready Gen AI Systems

Table of Contents

Building Production-Ready Gen AI Systems

Architecture, Patterns & Governance for LLM Systems

Srinivas Bommena

5 Chapters 8 Patterns 12 Principles 34 Checklist Items 56 Pages Front Matter —

Preface

Why Gen AI systems fail differently from traditional software, scope declaration, and how to use this book.

—

A Note on Build vs Buy New

Foundation model access models, self-hosting breakeven calculation, and how to evaluate providers beyond accuracy.

Managed API vs self-hosted open-weight vs hybrid routing
Breakeven: 500M–2B tokens/month for a single A100 (with derivation)
Provider evaluation: support SLAs, rate limits, data commitment terms, model version stability
Fine-tuning vs prompt engineering cost crossover criteria

Chapter 1 01

The AI Maturity Model

Assessing organisational readiness before committing to architecture.

L1 Foundational — direct API, no gateway, no evaluation
L2 Contextual — RAG pipeline, prompt registry, gateway layer
L3 Agentic — tool calling, full observability, CI eval harness
L4 Adaptive — fine-tuned specialists, multi-agent mesh, continuous feedback
Capability coverage table across all four levels
Pre-engagement maturity assessment framework

Chapter 2 02

Canonical AI System Architecture

The six-layer reference blueprint for production AI systems.

Layer 1: Clients — Web/Mobile UI, CLI/API consumers, Event Bus triggers
Layer 2: Gateway — AI Gateway, Guardrail Engine (input and output)
Layer 3: Orchestration — Prompt Registry, Agent Runtime, Memory Manager
Layer 4: Models — Foundation model, Specialist/Fine-tuned, Embedding model
Layer 5: Data — Vector store trade-offs, Structured store, Object storage, Chunking strategy
Layer 6: Eval & Ops — Evaluation harness, Observability stack, Feedback loop

Chapter 3 03

Design Patterns

Eight reusable architectures from simple retrieval to multi-agent systems. Each pattern includes When to Use criteria, Strengths, Limitations, and code skeleton.

Pattern 1: Naive RAG · Retrieval · L2 · Low complexity
Pattern 2: Advanced RAG · Hybrid search, cross-encoder re-ranking · L2–L3 · Medium
Pattern 3: ReAct Agent · Reason + Act loop with step budget · L3 · High
Pattern 4: Plan-and-Execute Agent · DAG decomposition · L3 · High
Pattern 5: Structured Output Pipeline · Schema-validated extraction · L2–L3
Pattern 6: Fine-Tuning / PEFT · LoRA/QLoRA specialists · L4 · Very High
Pattern 7: Evaluation-Driven Iteration · All levels · Quality discipline
Pattern 8: Multi-Agent Collaboration · Specialist mesh · L4 only

Chapter 4 04

Architecture Principles

Twelve non-negotiable standards distilled from production failures. Each principle includes an example and an anti-pattern.

P01 Version Everything — prompts, models, embeddings, datasets
P02 Separate Concerns by Layer — no direct model API calls from application code
P03 Design for Failure — retries, circuit breakers, model drift, hallucination cascade, tool schema drift
P04 Guardrails Are Not Optional — direct and indirect prompt injection, input/output gates
P05 Evaluate Before You Ship — golden set sizing, statistical confidence, held-out test sets
P06 Instrument Every Call — OpenTelemetry, LangSmith, cost attribution
P07 Minimise Context Surface — context budget, compression, large-window trade-offs
P08 Scope Agent Authority — least-privilege manifests, step budgets, human-in-the-loop
P09 Own the Embedding Contract — index immutability, dual-write migration
P10 Isolate Tenant Data — collection-level isolation, application-layer anti-pattern
P11 Capture Feedback Continuously — ground truth expansion, PII stripping
P12 Document Architecture Decisions — AIADRs, regulatory classification

Chapter 5 05

The 34-Point Engagement Checklist

Quality gates from pre-engagement through production go-live. Every item requires evidence, not acknowledgement.

Phase 1: Pre-Engagement (items 01–06) — objectives, data inventory, maturity level, model selection, compliance, eval golden set
Phase 2: Architecture & Design (items 07–14) — gateway, guardrails, chunking strategy, tenant isolation, agent step budget, prompt registry, memory TTL, fallback
Phase 3: Delivery & Handover (items 15–34) — CI eval harness, observability, feedback capture, AIADRs, load testing, security review, runbook, rollback, data retention, cost alerts, DR test, UAT, production readiness sign-off

Appendices A

Glossary of Key Terms 25 terms

AIADR, Agent Runtime, Circuit Breaker, Cross-Encoder, DAG, Embedding Model, Evaluation Harness, Foundation Model, Gen AI, Guardrail Engine, Golden Eval Set, LoRA/QLoRA, Namespace Isolation, OpenTelemetry, PEFT, PII, Prompt Registry, RAG, RAGAS, ReAct, Step Budget, Tenant Isolation, TTL, Vector Store, and more.

Regulatory Mapping — Quick Reference 12 principles

All twelve architecture principles mapped to NIST AI RMF functions, ISO 42001 clauses, and EU AI Act articles. Includes compliance depth note and EU AI Act risk tier guidance.

Case Study: Autonomous M&A Due Diligence Agent Full worked example

Every framework section applied to a single complex agentic system for a mid-market private equity firm processing 5,000–40,000 documents per deal.

A1: Scenario brief — stakeholders, constraints, success metrics
A2: Maturity assessment — L2 current state, L3 target, L4 roadmap
A3: Architecture decisions — all six layers with rationale
A4: Pattern selection — four adopted, two explicitly rejected with reasons
A5: Principles in practice — five most critical with implementation evidence
A6: Completed 34-point checklist — all items evidenced; one item marked DEFERRED with rationale

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