Applications and practical exercises with embeddings
Now that you’ve mastered the mechanics of generating and querying embeddings, it’s time to apply these skills to real-world problems. In this section, we’ll explore practical use cases, such as semantic document search, personalized recommendations, and FAQ matching, where embeddings add tangible value. Each example is paired with an exercise challenge designed to help you practice implementing a solution.
Before you dive into the exercises, approach each problem as a system designer. Keep these steps in mind:
- Define the problem: Clearly outline the problem you want to solve, whether it’s matching job descriptions with candidates, classifying documents, or developing a recommendation system.
- Gather data: Collect the necessary data for your application. For example, gather job descriptions and candidate resumes, or collect a dataset of documents or items.
- Generate embeddings...
