Architecting Generative AI Applications PDF Download – Leonid Kuligin
Architecting Generative AI Applications Summary and Overview
The rapid rise of generative AI has created new possibilities and new headaches for software architects and developers. “Architecting Generative AI Applications” by Leonid Kuligin offers a pragmatic, experience-based guide to designing systems that harness the power of large language models and diffusion models responsibly. This PDF goes beyond prompting tutorials to address the full lifecycle of production applications.
Readers will learn how to structure applications that combine generative capabilities with traditional software components, manage context windows and token costs, implement robust evaluation and monitoring, and handle the unique failure modes of probabilistic outputs. The author discusses patterns for retrieval-augmented generation, agentic workflows, and human-in-the-loop designs that improve reliability. Security, privacy, and compliance considerations receive thorough treatment.
What distinguishes this book is its focus on real-world tradeoffs rather than idealized architectures. Kuligin shares lessons from deploying generative systems at scale, including when to fine-tune versus when to rely on prompting and retrieval. The PDF includes diagrams, code examples, and decision frameworks that teams can adapt to their own projects. Whether you are a senior engineer evaluating generative AI for your stack or a technical leader planning a new product, this guide provides the architectural thinking needed to move from experimentation to dependable, maintainable systems.
PDF Book Details and Analysis
| 📖 Book Title: | Architecting Generative AI Applications |
| ✍️ Author: | Leonid Kuligin |
| 📁 Category: | Technology, Artificial Intelligence, Software Development, Generative AI |
| 🌍 Language: | English |
| 📄 File Type: |
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