Building Agent-Powered Applications
Your guide to generative AI, RAG, fine-tuning, and orchestration for production use
Created by Vasyl Zvarydchuk
Discover how to turn large language model prototypes into reliable, production-ready AI applications. Explore the full process from understanding model behavior to designing, building, and evaluating agent-powered systems for real-world use. Gain practical skills to create scalable and trustworthy generative AI solutions.
Packt | Apr 2026 | 490 min
What You Will Learn
You will start by building a foundation in AI, NLP, and large language models, then move into prompt engineering and real-world NLP tasks. As you progress, you will explore retrieval-augmented generation, fine-tuning, and agent design, always focusing on practical decisions and trade-offs for production systems. Evaluation techniques and responsible AI practices are woven throughout.
Key Features
- Combine prompting, retrieval, fine-tuning, and agents for robust AI apps
- Evaluate reliability and quality using practical metrics and testing methods
- Design agentic workflows with tools, memory, and orchestration for production
Target Audience
Ideal for AI engineers, data scientists, software engineers, and technical leads ready to build production-grade generative AI systems. If you have experience with Python and core software engineering concepts and want to move from traditional software or classical machine learning into applied AI with LLMs and agents, this guide is designed for you.
