production-agentic-rag-course: notebooks to production

TL;DR: A repository named production-agentic-rag-course carries no GitHub description, but its file tree says a lot. It pairs Jupyter notebooks with a real Python package, adds Apache Airflow orchestration, Docker Compose, and a Gradio front end, and ships tests and pre-commit hooks. MIT licensed, 9,409 stars, last pushed 2026-06-05.

Reading the repository without a description

When the description field is empty, the root of the tree becomes your best source. This is what the API returns for the top level:

Path	What it implies
notebooks/	the teaching material
src/	a real package, not just loose scripts
tests/	the code is expected to be verified
airflow/	pipeline orchestration is part of the curriculum
gradio_launcher.py	a browser UI to drive the finished system
compose.yml, Dockerfile, Makefile	services run in containers
pyproject.toml, uv.lock	dependencies pinned with uv
.env.example, .env.test	configuration is templated, not ad hoc
.pre-commit-config.yaml	formatting and checks run before commit

That last group is what separates this from a notebook dump. A src directory next to notebooks is a deliberate split between the version you read and the version you run.

The language split tells the teaching style

GitHub’s language endpoint gives measured bytes per language, which is more honest than the single primary-language label:

curl -sL -H "Accept: application/vnd.github+json" \
  https://api.github.com/repos/jamwithai/production-agentic-rag-course/languages

{
  "Python": 383790,
  "Jupyter Notebook": 253146,
  "Dockerfile": 2705,
  "Makefile": 1403,
  "Shell": 844
}

Jupyter Notebook accounts for roughly 40 percent of the bytes. Python is the rest. So the instruction is delivered in notebooks, but the executed code is ordinary Python, which is the arrangement that makes a course repo usable as a reference after you have finished the course.

Orchestration is the differentiator

The airflow directory is the part most RAG tutorials skip. A retrieval pipeline that runs once from a notebook teaches retrieval. A pipeline scheduled by Airflow teaches what production actually asks of you: dependencies between stages, retries, backfills, and the ability to look at what ran yesterday.

Docker Compose and a Dockerfile alongside it mean the supporting services are meant to be started as a set rather than installed by hand, and the Gradio launcher gives you a UI over the result. A Makefile sits on top of that for the repetitive commands.

If you want the tree yourself before cloning anything, the contents endpoint is enough:

curl -sL -H "Accept: application/vnd.github+json" \
  https://api.github.com/repos/jamwithai/production-agentic-rag-course/contents/ \
  | jq -r '.[].name'

How current it is

This is the part to weigh before you plan around it. The repository was created on 2025-08-06 and last pushed on 2026-06-05, which is about four months before this writing. It is not abandoned, but it is not being kept in step with fast-moving libraries either.

Other numbers from the API:

Field	Value
Full name	jamwithai/production-agentic-rag-course
License	MIT
Stars	9,409
Forks	2,071
Open issues	29
Size	about 10 MB
Owner type	User

Twenty-nine open issues across 9,409 stars is a very low ratio, and 2,071 forks says plenty of people are running it locally rather than only reading.

When to use it

Good fit if you have built a RAG prototype from a notebook and hit the wall where nothing runs on a schedule, nothing is retried, and you cannot tell what happened overnight. This repository is an answer to that specific problem, and the Airflow layer is the part you are unlikely to get from a blog post.

It is also a reasonable skeleton to fork. MIT means you can drop it into your own project and swap the retrieval logic for your own, keeping the orchestration you would otherwise have to write.

When to skip it

Skip it if your goal is evaluating retrieval quality. The system is built around pipeline mechanics, so you will spend your time on Airflow and Docker rather than on chunking and ranking.

Mind the four-month gap if you plan to follow along with current library versions. Expect to spend time on dependency conflicts before you get to the actual material, and pin your environment early.

Source and Accuracy Notes

• Project page: https://github.com/jamwithai/production-agentic-rag-course
• Source repository: jamwithai/production-agentic-rag-course (verified via GitHub API html_url)
• License: MIT (verified via GitHub API license.spdx_id)
• Stars at time of writing: 9,409
• Additional verification: languages and contents API endpoints