Generative AI Certification Prep
A seven-video course covering the core topics for the Databricks Generative AI Associate exam, from prompt engineering and RAG to vector search and evaluation.
Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.
Now playing: Introduction to Vector Stores – Vector Databases for RAG (GenAI Course – 4)
This learning path follows the VectorMind Academy playlist in order. It starts with a prompt engineering primer, then moves through retrieval-augmented generation (RAG), data preparation, vector stores, Mosaic AI Vector Search, MLflow tracking, and evaluation. The videos are summary-level explanations rather than full hands-on labs, so treat this as a study companion. Watch in sequence, take notes, and pause to think through each concept. The playlist description mentions exam overview, MCQs, and revision sessions, but those items are not present in the seven lessons listed here.
Self-paced learners who want a clear free path with creator-credited YouTube lessons.
Lesson 1: Prompt Engineering Primer for Databricks Generative AI (GenAI Course – 1)
This opening video gives a primer on prompt engineering for the Databricks Generative AI Associate exam. It sets up the course and introduces how prompts are used with large language models in Databricks workflows. The description is truncated, so the exact subtopics covered are not fully clear from the source.
Open on YouTubeLesson 2: Introduction to RAG on Databricks – Retrieval-Augmented Generation (GenAI Course – 2)
This video introduces retrieval-augmented generation and explains how RAG helps LLMs use your own data. It is a concept-level walkthrough of the RAG module rather than a coding session.
Open on YouTubeLesson 3: Preparing Data for RAG – Chunking, Embeddings & Metadata (GenAI Course – 3)
This lesson covers preparing data for RAG solutions on Databricks. It focuses on what makes data RAG-ready, including chunking, embeddings, and metadata.
Open on YouTubeLesson 4: Introduction to Vector Stores – Vector Databases for RAG (GenAI Course – 4)
A summary-level explanation of vector stores (vector databases) used in RAG systems. The video aims for a simple conceptual understanding rather than detailed database tuning.
Open on YouTubeLesson 5: Mosaic AI Vector Search – Semantic Search on Databricks (GenAI Course – 5)
This video gives a summary-level explanation of Mosaic AI Vector Search and how it supports semantic search and RAG on Databricks. It is not a full configuration or notebook walkthrough.
Open on YouTubeLesson 6: MLflow for RAG Applications – Tracking & Managing LLM Pipelines (GenAI Course – 6)
A summary-style overview of how MLflow is used with RAG and LLM applications. It covers what MLflow does at a high level rather than every API.
Open on YouTubeLesson 7: Evaluating RAG Systems & Continual Learning – Metrics & Feedback (GenAI Course – 7)
The final Week 1 video gives a summary-level view of evaluating RAG applications and thinking about continual learning. It covers metrics and feedback at a conceptual level, not a full evaluation framework.
Open on YouTube0 of 7 lessons completed (0%).
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