The Hidden Foundation of GenAI gives you a clear start in embeddings. You learn what sits under LLMs, vector search, and semantic tools. The course is for data engineers who want to understand how embeddings work and why they matter.
You see how text turns into vectors and how systems measure similarity. You also use an interactive Embedding Playground and simple Python code. This helps you build trust in vector search tasks and RAG workflows.
This course is the first part of a GenAI series at the Academy. Later modules cover semantic search, vector databases, and a full project where you build a RAG pipeline.
What You Learn
- Clear grounding in embeddings without heavy math.
- Hands-on work with the Embedding Playground to see how text similarity works.
- A step-by-step view of how models turn text into vectors.
- Python practice with cosine similarity and both structural and semantic similarity.
- Real aspects of production use, such as tokens, LLM API cost, and workload impact.
