explaingit

ed-donner/llm_engineering

Analysis updated 2026-07-03

5,943Jupyter NotebookAudience · developerComplexity · 2/5Setup · easy

TLDR

Code and notebooks for an eight-week hands-on course on building real applications with large language models. Goes from running a local AI model for free to building an autonomous agent, with free Ollama and Google Colab alternatives at every paid step.

Mindmap

mindmap
  root((repo))
    Course structure
      Eight weekly modules
      Jupyter notebooks
      Pull requests welcome
    Local models
      Ollama setup
      No-cost GPU Colab
      Free path throughout
    API providers
      OpenAI
      Anthropic
      Hosted models
    End goal
      Autonomous agent
      Real projects
      Practitioner skills
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Work through eight weekly modules to go from beginner to confident LLM practitioner by running and modifying every notebook rather than reading theory.

USE CASE 2

Run a local AI model on your own computer for free using Ollama, then connect to hosted APIs from OpenAI and Anthropic for more capable models.

USE CASE 3

Access GPU hardware at no cost via Google Colab for the later modules that require running larger models.

USE CASE 4

Build an autonomous AI agent in Week 8 that can plan and take actions on its own, using the step-by-step notebooks provided.

What is it built with?

PythonJupyter NotebookOllamaOpenAI APIAnthropic APIGoogle Colab

How does it compare?

ed-donner/llm_engineeringcrewaiinc/crewai-examplessnakers4/silero-models
Stars5,9435,9445,917
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultyeasymoderateeasy
Complexity2/53/52/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

Total API cost across the full course is a few dollars, free alternatives are provided for every paid step.

License not specified in the explanation.

In plain English

This repository holds the code and notebooks for an eight-week course called "LLM Engineering" by Edward Donner, available on Udemy. The course teaches how to build real applications using large language models (AI systems like GPT and Claude), aimed at people who want to go from beginner to confident practitioner through hands-on projects. The course is structured as eight weekly modules, each building on the last. It starts with running a local AI model on your own computer using a free tool called Ollama, then progresses through using hosted AI APIs from providers like OpenAI and Anthropic, and eventually reaches Week 8 where students build an autonomous AI agent that can take actions on its own. From Week 3 onward, some exercises use Google Colab, a free cloud environment that provides access to GPU hardware for running larger models. API costs are kept intentionally small. The instructor estimates a few dollars total across the entire course, and provides free alternatives (Ollama for local models, Google Colab for cloud GPUs) for every paid step. For the optional Week 7 section that involves training a model, the instructor personally spent about $10 but notes it is not required. The repository was fully refreshed in December 2025 with new course content. Students who want to follow along with the original video recordings can check out an older branch called "original" via git. Each weekly folder contains Jupyter notebooks with code that students are expected to run, modify, and experiment with. The course places emphasis on doing rather than watching: students are encouraged to tweak every example and submit their own solutions as pull requests. The repo is organized into week folders, a guides directory with supplementary notebooks, and a setup directory with installation instructions for all platforms.

Copy-paste prompts

Prompt 1
Using the llm_engineering Week 1 notebook as a guide, set up Ollama locally and run a small open-source model to answer questions about a text document I provide.
Prompt 2
Based on the Week 3 notebooks in llm_engineering, write a Python script that sends the same prompt to both the OpenAI API and the Anthropic API and prints the responses side by side.
Prompt 3
Following the llm_engineering Week 8 autonomous agent pattern, build an agent that can search the web, summarize results, and email me a daily briefing.
Prompt 4
Using the llm_engineering course structure, create a Jupyter notebook that fine-tunes a small open-source model on a custom dataset using Google Colab's free GPU tier.

Frequently asked questions

What is llm_engineering?

Code and notebooks for an eight-week hands-on course on building real applications with large language models. Goes from running a local AI model for free to building an autonomous agent, with free Ollama and Google Colab alternatives at every paid step.

What language is llm_engineering written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, Ollama.

What license does llm_engineering use?

License not specified in the explanation.

How hard is llm_engineering to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is llm_engineering for?

Mainly developer.

Open on GitHub → Explain another repo

This repo across BitVibe Labs

Scan in gitsafehub Deploy in gitdeployhub ed-donner on gitmyhub

Verify against the repo before relying on details.