LLM Application Development With Python (Learning Path) - Real Python

"By completing this path, you'll be able to call LLM APIs from OpenAI, Ollama, and OpenRouter in your Python code, write effective prompts that produce reliable, structured results, and build retrieval-augmented generation (RAG) pipelines with LlamaIndex, ChromaDB, and LangChain."

"You'll start by calling model APIs directly, then move into prompt engineering, RAG pipelines, agent frameworks, and finish by connecting your agents to external tools through MCP."

"This path is for Python developers who want to build applications on top of language models. You should be comfortable with Python basics and working with APIs."

This learning path enables Python developers to integrate large language models into applications. It covers calling LLM APIs, writing effective prompts, and building retrieval-augmented generation pipelines. Developers will learn to convert documents into LLM-ready formats, create stateful AI agents, and connect these agents to external tools using MCP servers. The path is designed for those comfortable with Python basics and API usage, progressing from API calls to advanced agent frameworks.

Read at Realpython