DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to build a retrieval corpus from the freight case study with AI Engineering Lab

Use the Week 1 seeded freight data as the retrieval corpus for a RAG exercise.

  • Research
  • Intermediate
  • 120 minutes
  • From the README
  • Any agent

Who it is forAn analyst or engineer on a team preparing for retrieval augmented generation work, triggered by needing a realistic, shared dataset to practice on.

The problemRetrieval exercises are hard to learn on toy text, and separate datasets for each lesson make it hard to see how skills connect.

The outcomeA working retrieval step over the ZoroLogistics dataset, with a metric and an error note as the course requires from Week 3 on.

What you need first

  • A computer you can install software on
  • Git installed, to run the clone command
  • Python installed, with pip available
  • About ten hours a week and basic computer literacy
  • No prior Python, GPU, or paid API key is required

Steps

  1. Run git clone https://github.com/zorost/AI-Engineering-Lab.git
  2. Run cd AI-Engineering-Lab
  3. Run python -m pip install -r requirements.txt
  4. Complete Week 1 to generate the dataset, then open the Week 7 folder under curriculum/
  5. Build a small retrieval index over the generated tables and record one metric and one error note (inferred, check the README)

Prompt for your agent

In the AI-Engineering-Lab repo, complete curriculum/week-01 to generate the freight dataset under data/, then build a small retrieval index over it for the Week 7 exercise. Return the retrieval query you tested, the metric you chose, and one error note.

Caveat. The README names Week 7 as the retrieval corpus step, but the retrieval library and method are described in the week folder, not in the README.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page