Frontmatter

The proliferation of powerful Large Language Models (LLMs) and other generative techniques has initiated a fundamental transformation in programming and software engineering in general. The emergence of the Generative AI (GenAI) made possible by these LLMs has transformed programming from the act of instructing computers through explicit rules to teaching them to simulate intelligence through examples. The confluence of open-source tools for running LLMs locally and the development of small and highly efficient LLMs which do not require special hardware has profoundly democratized Generative AI itself, enabling developers to be more productive overall throughout the Software Development Lifecycle (SDLC). In essence, the combination of efficient open-source models, seamless tooling and easily accessible hardware has made GenAI a programmable resource for every developer, rather than a precious utility only available to large tech corporations.

This monograph provides a comprehensive and example-driven introduction to generative AI in terms of practical software engineering. It walks readers through core concepts of language models, including: prompt engineering, retrieval-augmented generation (RAG), agent architectures, and code generation - all while grounding each topic in executable Python examples and real development workflows. We will interactively analyze the foundational mechanisms enabling high-quality code generation, automated refactoring, and AI-driven testing. Key topics discussed will also include the architecture of code-specific LLMs, the mechanics of RAG for contextual grounding, and the design of agentic planning models for complex tasks.

This material places a strong emphasis on hands-on learning, and is designed for students & software engineers who want to build mental models of GenAI behavior and understand how to effectively integrate GenAI into their development processes. By making GenAI concrete and programmable, this executable monograph aims to lower the barrier to entry for anyone wanting to augment their programming productivity with GenAI.