tailwindlabs / tailwindcss
github.com·23h ago
Developer Tools
Real Python·Jan 1·8 min read
Learning Path: LLM Application Development With Python
TL;DR
Developers building AI applications often lack practical guidance on integrating large language models (LLMs—AI systems trained on vast text) into production Python code. This learning path teaches how to call model APIs, write effective prompts, add retrieval-augmented generation (RAG—fetching external data to improve answers), build AI agents (autonomous systems that plan and act), and connect via Model Context Protocol (MCP—a standard for tool integration).
✦ Why It Matters
Engineers can immediately apply Python patterns to deploy LLM features—APIs, RAG, agents, MCP—in production applications.
Key Takeaways
- Learn to call LLM APIs from OpenAI and Ollama.
- Master prompt engineering for structured results.
- Build RAG pipelines with LlamaIndex and ChromaDB.
- Create stateful AI agents using Pydantic AI.
Full Summary
Developers can learn to harness the power of large language models (LLMs) through a structured learning path focused on Python. The curriculum includes calling LLM APIs from providers like OpenAI and Ollama, crafting effective prompts for reliable outputs, and building retrieval-augmented generation (RAG) pipelines using tools like LlamaIndex and LangChain.
Participants will also explore converting documents into LLM-ready formats with MarkItDown and creating stateful AI agents using Pydantic AI and LangGraph. The path culminates in connecting these agents to external tools via Model Context Protocol (MCP) servers.
By completing this training, developers will be equipped to build applications that utilize LLMs for enhanced functionality and user interaction.