Retrieval and RAG
Advanced Retrieval-Augmented Generation
Explore advanced RAG patterns and evaluation, including measuring and improving retrieval performance.
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Course topics
Retrieval and RAG
- Robust LLM calls
- Evaluation datasets
- Retrieval performance
You check the sources
Find your fit
Is this a useful starting point?
For experienced software engineers with Python, data-science basics and prior RAG practice; Docker and paid tools are listed.
- Starting point
- Expert
- Course length
- 10.5 total hours
- Lessons
- 60 lectures
Course by Rémi Connesson | Python - Data Science - Machine Learning - Deep Learning
Course last updated: 06/2025
Requirements
- Have Docker installed on your machine
- Access to a modern powerful laptop with python installed or a Google Drive account
- Working experience as a Software Engineer, preferrably more than two years
- At least intermediate Python Programming or the ability to learn it fast (eg: Seniority in another Programming Language)
- Willing to spend about ten dollars for running the LLM calls (either locally or through OpenAI)
- Access to pro version of ChatGPT (or equivalent)
- Basic of data science (precision, recall, pandas)
- Ability to debug by yourself, especially typos (we will use async code, you must be comfortable reading tracebacks)
- You know what RAG means and have already implemented Basic or Naive RAG in a tutorial, at least
What is included
- 10.5 hours on-demand video
- 10 articles
- 7 downloadable resources
- Access on mobile
- Certificate of completion
DoJo membership: A$250 per month per seat, excluding GST. Includes fortnightly group support and a private member community.
Explore the lessons
Course curriculum.
6 sections • 60 lectures • 10h 32m total length
Introduction & Setup8 lectures • 40min
- Introduction20:45
- BEFORE YOU BUY: The course is not finished and new videos are added every weeks00:04
- How To Follow This Course00:43
- Create an OpenAI API Key03:34
- Setup Vitual Env and Jupyter04:34
- Setup local Langfuse with Docker05:12
- How to get the Python `.gitignore` for the next video00:10
- Initialize a git repo for our code04:58
Section 1 - Making our LLM powered systems more Robust20 lectures • 4hr 7min
Lecture details are available inside DoJo.
Section 2 - Measure and Improve Performance of the Retrieval System1 lecture • 20min
Lecture details are available inside DoJo.
Section 2 - Part 1 - [STEP BY STEP] Generating a Synthetic Evaluation Dataset21 lectures • 4hr 46min
Lecture details are available inside DoJo.
Section 2 - Part 2 - Measuring Improvements in Retrieval Performance9 lectures • 39min
Lecture details are available inside DoJo.
Conclusion1 lecture • 1min
Lecture details are available inside DoJo.
From learning to practice
Try this next.
Build a small evaluation dataset and compare retrieval changes against the same questions.
A useful next step
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