Building LLM Agents in Python: Prompting and Graph Orchestration

Large Language Models offer immense potential, but turning raw API access into reliable, multi-step applications requires specialized development skills. This course teaches you the practical techniques needed to build sophisticated LLM workflows. By the end of this course, you will be able to integrate LLMs into your Python applications, implement advanced prompting strategies for reliable data extraction, and architect autonomous AI agents capable of complex decision-making and function calling. What you'll learn: * Understand the fundamentals of LLM interaction using Python SDKs and direct API calls. * Master prompt engineering techniques, including instruction tuning and few-shot learning. * Apply Structured Output methods using data models (like Pydantic) to ensure reliable data extraction from LLMs. * Practice implementing basic Retrieval-Augmented Generation (RAG) patterns for contextual grounding. * Configure AI agents capable of using external tools via Function Calling and tool definitions. * Design and implement multi-step agent architectures using graph-based orchestration tools like LangGraph. The course begins with foundational concepts and API integration, progresses through advanced prompting and structured data handling, and culminates in the development of a complete, stateful AI agent architecture. This course is designed for beginner Python developers who want to specialize in building practical, production-ready applications powered by Large Language Models. No prior experience with AI or machine learning is required. Start building the next generation of intelligent applications today.