Intermediate Python and Large Language Models

Building useful applications with large language models takes more than sending a prompt to an API. It also means handling context, retrieving the right information, connecting tools, and making the finished system work in practice. In Intermediate Python and Large Language Models, Dilyan Grigorov uses Python and LangChain to explore that broader development process, from core components to applied projects.

Start with LangChain’s building blocks

The book opens with LangChain fundamentals, including prompts, chains, memory, tools, agents, and retrieval-augmented generation (RAG). It then develops these ideas through more advanced components such as document loaders, embedding models, indexes, and retrievers. Together, these topics show how separate pieces can contribute to an LLM workflow.

Learn through practical application

Much of the book is organized around building applications. Projects include a YouTube video summarizer, a chat interface for a GitHub repository, financial-report analysis, and tools for tasks such as content generation and codebase analysis. These examples offer readers a concrete way to follow how Python, LangChain, and language-model services can be brought together.

Explore deployment and model development

Beyond application-building, later chapters cover deploying LLM-powered software and building and fine-tuning language models. This wider scope connects the design of individual features with questions about operating and developing AI systems.

For Python developers moving into LLMs

This title is directed toward software engineers and Python developers interested in creating large-language-model applications. Readers looking for a hands-on route through LangChain concepts, retrieval workflows, and project examples will find its emphasis on implementation central to the book.