Building with Generative AI

Building with Generative AI

A Practical Guide to LLM Products, Retrieval-Augmented Generation, and Enterprise Knowledge Systems

By Ritesh Sonvane

Generative AI is changing how software is designed, developed, and deployed. Large language models (LLMs) now make it possible to build applications that understand natural-language requests, analyze documents, generate structured outputs, and help organizations turn scattered information into useful knowledge. But building a reliable AI product requires much more than connecting an application to a model API.

Building with Generative AI is a practical guide to designing, developing, evaluating, and deploying AI-powered applications for real-world use. It takes you from the fundamentals of large language models to production-oriented architectures, retrieval-augmented generation (RAG), and enterprise AI systems.

Whether you are a software developer, AI practitioner, technical founder, product manager, or enterprise architect, this book will help you understand the technologies, engineering decisions, and operational practices behind useful generative AI products.

Inside this book, you will discover how to:

Understand large language models: Learn how LLMs work, how tokenization and context windows influence model behavior, and why generated answers sometimes contain errors.

Choose the right AI model: Evaluate open, closed, and hybrid model options according to capability, cost, latency, context requirements, and deployment needs.

Design effective prompts and structured outputs: Build more consistent interactions and integrate model responses into software workflows.

Architect LLM applications: Connect models, application backends, data sources, tools, and user interfaces into coherent AI-powered systems.

Identify valuable AI use cases: Translate real business problems into practical applications with measurable outcomes.

Build conversational and agentic experiences: Understand how AI assistants interact with users, tools, and external services.

Evaluate AI quality and reliability: Measure answer accuracy, relevance, safety, and consistency while identifying failure modes before deployment.

Implement retrieval-augmented generation (RAG): Ground model responses in external knowledge sources instead of relying exclusively on information learned during training.

Build effective retrieval pipelines: Work with document ingestion, chunking, metadata, embeddings, vector search, hybrid retrieval, reranking, and query rewriting.

Generate evidence-based answers: Design grounded responses with meaningful citations and traceable supporting information.

Prepare AI systems for enterprise use: Address security, privacy, access control, governance, responsible AI, monitoring, and continuous improvement.

Develop a production-oriented RAG assistant: Bring the concepts together in a practical project that connects retrieval, model integration, answer generation, and evaluation.

Move beyond prompts and build complete AI products.

This book emphasizes practical engineering principles rather than treating generative AI as a collection of isolated tools or techniques. You will learn why model selection matters, how retrieval can improve access to organization-specific knowledge, where conventional software logic remains essential, and how systematic evaluation helps manage the limitations of probabilistic AI systems.

You will also explore the trade-offs among response quality, computational cost, latency, data protection, and operational complexity—the decisions that often determine whether an AI prototype becomes a dependable product.

The chapters progress from foundational concepts to application architecture, advanced RAG strategies, enterprise deployment, and a step-by-step implementation project. A glossary, an evaluation checklist for RAG systems, and a tool and framework reference provide additional support.
Build with understanding. Design for reliability. Deploy with confidence