LLM Application Development With Python
Learning Path ⋅ Skills: OpenAI, Ollama, OpenRouter, Prompt Engineering, LangChain, LlamaIndex, ChromaDB, MarkItDown, RAG, Embeddings, Pydantic AI, LangGraph, MCP
Large language models can do much more than answer questions in a chat window. This learning path teaches you to integrate LLMs into Python applications, from API calls to autonomous agents.
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
- Build retrieval-augmented generation (RAG) pipelines with LlamaIndex, ChromaDB, and LangChain
- Convert documents into LLM-ready formats with MarkItDown
- Create stateful AI agents using Pydantic AI and LangGraph
- Connect agents to external tools and data sources using MCP servers
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.
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.
LLM Application Development With Python
Learning Path ⋅ 13 Resources
Connect to LLM APIs
Start by learning how to call large language models from Python, whether through cloud APIs or local inference.
Course
Leverage OpenAI's API in Your Python Projects
Learn how to use the ChatGPT API with Python's openai library to send prompts, control AI behavior with roles, and get structured outputs.
Tutorial
How to Integrate Local LLMs With Ollama and Python
Learn how to integrate your Python projects with local models (LLMs) using Ollama for enhanced privacy and cost efficiency.
Course
Accessing Multiple AI Models With the OpenRouter API
Access models from popular AI providers in Python through OpenRouter's unified API with smart routing, fallbacks, and cost controls.
Craft Effective Prompts
Learn how to write prompts that get reliable, structured results from language models.
Tutorial
Prompt Engineering: A Practical Example
Learn prompt engineering techniques with a practical, real-world project to get better results from large language models. This tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.
Work With LLM Frameworks
Use LangChain to build reusable chains and pipelines around language models.
Course
First Steps With LangChain
Large language models (LLMs) have taken the world by storm. In this step-by-step video course, you'll learn to use the LangChain library to build LLM-assisted applications.
Add Retrieval‑Augmented Generation (RAG)
Ground your LLM apps in real data using embeddings, vector databases, and retrieval pipelines.
Course
Using LlamaIndex for RAG in Python
Learn how to set up LlamaIndex, load your data, build and persist an index, and run queries to get grounded answers with RAG in Python.
Course
Vector Databases and Embeddings With ChromaDB
Learn how to use ChromaDB, an open-source vector database, to store embeddings and give context to large language models in Python.
Tutorial
Python MarkItDown: Convert Documents Into LLM-Ready Markdown
Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds.
Tutorial
Build an LLM RAG Chatbot With LangChain
Large language models (LLMs) have taken the world by storm, demonstrating unprecedented capabilities in natural language tasks. In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j.
Build AI Agents
Go beyond single prompts and build agents that reason, maintain state, and use tools.
Course
Building Type-Safe LLM Agents With Pydantic AI
Build type-safe LLM agents in Python with Pydantic AI using structured outputs, function calling, and dependency injection.
Tutorial
LangGraph: Build Stateful AI Agents in Python
LangGraph is a versatile Python library designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph.
Connect Agents to External Tools With MCP
Use the Model Context Protocol to give your agents access to databases, APIs, and files.
Course
Connecting LLMs to Your Data With Python MCP Servers
Build an MCP server in Python that exposes tools, resources, and prompts so AI agents like Cursor can interact with your data.
Course
Testing MCP Servers With a Python MCP Client
Learn how to build a Python MCP client that tests MCP servers from your terminal. List their tools, prompts, and resources, then call each one.
Congratulations on completing this learning path! You can now call LLM APIs, build RAG pipelines, create AI agents, and connect them to external tools using MCP.
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