The Hidden Foundation of GenAI
Course description
Generative AI is everywhere today, but few understand the fundamental concepts it's built upon. "The Hidden Foundation of GenAI" is a starting point for those who want to truly understand what lies behind LLM, vector search, and semantic understanding. This course is specifically designed for data engineers and focuses on embeddings—one of the most important (and most misinterpreted) building blocks of any GenAI system.
Instead of overloading with mathematical theory, we provide practical insights: how text is converted into vectors, how similarity is calculated, and how this underlies scenarios like semantic search and Retrieval-Augmented Generation (RAG). You will work with an interactive Embedding Playground, analyze Python examples, and gain the confidence to use vector search in your own projects.
This course opens a series of sessions on GenAI at the Academy. In the following modules, you will continue exploring semantic search, vector databases, and complete your journey with a full-fledged project—implementing a GenAI pipeline with RAG.
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What awaits you in the course:
- Clear and practical introduction to embeddings without excessive terminology.
- Working with Embedding Playground and understanding the mechanics of text similarity.
- Step-by-step breakdown of converting text into vectors and the role of embedding models.
- Practice in Python: cosine similarity, the difference between structural and semantic similarity.
- Real-world aspects: tokens, the cost of LLM API requests, and the impact of this on production workloads.
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All Course Lessons (9)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Intro to the GenAI Track: Practical Foundations for Data Engineers Demo | 00:25 | |
| 2 | Embeddings in Action: Playground, Search, and RAG | 01:47 | |
| 3 | Hands-On with Embeddings: Comparing Text Similarity | 02:28 | |
| 4 | Understanding Similarity: From Angles to Embedding Scores | 02:14 | |
| 5 | Text Structure vs. Meaning: Understanding Embedding Scores | 02:23 | |
| 6 | Why Your Embedding Model Matters (A Lot) | 02:34 | |
| 7 | Understanding Tokens: From Text to Vectors to Cost | 03:43 | |
| 8 | Embedding Walkthrough: Real Data in Semantic Search and RAG Pipelines | 04:20 | |
| 9 | That’s It, you Know Enough to Build | 00:48 |
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