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.

Tutorial

How to Integrate ChatGPT's API With Python Projects

Learn how to use the ChatGPT Python API with the openai library to build AI-powered features in your Python applications.

Interactive Quiz

How to Integrate ChatGPT's API With Python Projects

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.

Interactive Quiz

How to Integrate Local LLMs With Ollama and Python

Podcast

Running Local LLMs With Ollama and Connecting With Python

Tutorial

How to Use the OpenRouter API to Access Multiple AI Models via Python

Access models from popular AI providers in Python through OpenRouter's unified API with smart routing, fallbacks, and cost controls.

Interactive Quiz

How to Use the OpenRouter API to Access Multiple AI Models via Python

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.

Interactive Quiz

Practical Prompt Engineering

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.

Interactive Quiz

First Steps With LangChain

Add Retrieval‑Augmented Generation (RAG)

Ground your LLM apps in real data using embeddings, vector databases, and retrieval pipelines.

Tutorial

LlamaIndex in Python: A RAG Guide With Examples

Learn how to set up LlamaIndex, choose an LLM, load your data, build and persist an index, and run queries to get grounded, reliable answers with examples.

Interactive Quiz

LlamaIndex in Python: A RAG Guide With Examples

Tutorial

Embeddings and Vector Databases With ChromaDB

Vector databases are a crucial component of many NLP applications. This tutorial will give you hands-on experience with ChromaDB, an open-source vector database that's quickly gaining traction. Along the way, you'll learn what's needed to understand vector databases with practical examples.

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.

Interactive Quiz

Python MarkItDown: Convert Documents Into LLM-Ready Markdown

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.

Interactive Quiz

Build an LLM RAG Chatbot With LangChain

Podcast

Leveraging Documents and Data to Create a Custom LLM Chatbot

Build AI Agents

Go beyond single prompts and build agents that reason, maintain state, and use tools.

Tutorial

Pydantic AI: Build Type-Safe LLM Agents in Python

Learn how to use Pydantic AI to build type-safe LLM agents in Python with structured outputs, function calling, and dependency injection patterns.

Interactive Quiz

Pydantic AI: Build Type-Safe LLM Agents in Python

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.

Interactive Quiz