Examples and exercises

Last updated on 2025-10-14 | Edit this page

Estimated time: 65 minutes

Overview

Questions

  • How can I apply this knowledge?

Objectives

  • Explore, experiment, showcase

We’ve covered the basics of what an API is, how to interact with it and looked at two API specifications. It is now time to put this knowledge into practice. Below you’ll find links to language-specific scripts with GenAI use cases. We also provide additional links for further reading.

Discussion

In pairs or groups of 3, use the available data to come up with a generative AI (research) application - possible outcomes can be tables, graphs, summaries, or small pieces of software (advanced). Ideally, you can share these outcomes with the other workshop participants at the end of the exercise.

Example 1: SDG classification

We read a subset of publications and abstracts, and use structured outputs to retrieve labels for publication titles and abstracts.

Example 2: RAGs

Retrieval-augmented generation (RAG) can be a powerful tool to generate more reliable output. By providing a set of domain-specific documents, the LLM is able to query those documents before generating its answer to the user.

To implement RAG, you will need to use an embeddings model next to the LLM, e.g. text-embedding-ada-002.