CF

The Hidden Foundation of GenAI

20m 42s
English
Paid

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.

Additional

About the Author: Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

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#1: Intro to the GenAI Track: Practical Foundations for Data Engineers
All Course Lessons (9)
#Lesson TitleDurationAccess
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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Frequently asked questions

What are the prerequisites for enrolling in this course?
The course is designed for data engineers who want to understand embeddings, but it does not require a deep mathematical background. Familiarity with Python is recommended, as the course involves coding exercises that utilize Python to explore concepts like cosine similarity and text embedding.
How does this course compare to others in the GenAI series?
This course is the initial module in the GenAI series offered by the Academy. It focuses specifically on embeddings, providing foundational knowledge. Later courses in the series build on this by covering semantic search, vector databases, and a complete RAG pipeline project, expanding the practical applications of the concepts introduced here.
What practical skills will I gain from this course?
Students will gain hands-on experience with the Embedding Playground, learning how text turns into vectors and how similarity is measured. The course provides practical knowledge of Python for comparing text similarity, understanding tokenization, and evaluating model costs in real-world scenarios.
What is the target audience for this course?
The course is targeted at data engineers who seek to understand the underlying mechanics of embeddings in GenAI applications. It is tailored for those interested in how embeddings support vector search and semantic tools, rather than for individuals looking for a broader overview of general AI technologies.
What specific tools or platforms will be used in this course?
The course includes hands-on work with the Embedding Playground, an interactive tool that helps visualize how text similarity works. Additionally, Python is used extensively to practice concepts like cosine similarity and to explore both structural and semantic similarity in text embeddings.
What topics are not covered in this course?
The course does not cover advanced mathematical theories behind embeddings or detailed discussions of vector databases and full RAG pipelines. These topics are reserved for later modules in the GenAI series, which build on the foundational knowledge provided here.
How much time will I need to commit to complete this course?
The course consists of 9 lessons. Although the exact runtime is not specified, students should allocate enough time to engage with the interactive tools and complete the Python exercises. The commitment may vary depending on the individual's pace and familiarity with the subject matter.