Flagship program #4 Enterprise AI

Building Knowledge-Aware AI: RAG, Vector Databases & Knowledge Graphs

Ground LLMs in trusted knowledge — from embeddings and hybrid search to GraphRAG and expert memory.

0.0 (0) 0 learners Advanced English 30–40 hours Developers / data professionals Dr. Sara Al-Mansouri

What you'll learn

Explain why LLMs need external knowledge
Build embeddings, chunking and vector indexes
Implement a RAG pipeline with citations
Apply hybrid search, re-ranking, query expansion and multi-hop retrieval
Model knowledge graphs and combine them with vectors (GraphRAG)
Design user, course and expert memory
Learn with the Smart Instructor

This course has its own AI knowledge space. The Smart Instructor understands the syllabus, lessons and trainer materials, answers from them with citations, generates practice and adapts difficulty.

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Curriculum

9 sections · 25 lessons · 5.5 hours · 1 assessments

Semantic Similarity 6 min
Vector Representations 6 min
Chunking 7 min

Indexing 6 min
Retrieval 6 min
Metadata 6 min

The RAG Pipeline 6 min
Prompting with Evidence and Citations 6 min

Hybrid Search 7 min
Re-Ranking 6 min
Query Expansion 6 min
Multi-Hop Retrieval 6 min
Evaluating RAG 6 min

Entities 6 min
Relationships 6 min
Graph Traversal 6 min
Semantic Networks 6 min

Graph + Vector Retrieval 6 min
Community Knowledge 6 min
Relationship Reasoning 6 min

User Memory 6 min
Course Memory 6 min
Expert Memory 6 min

Final Project: Build & Submit AI Lab 180 min

Description

Enterprise AI is only as good as the knowledge it can reach. This flagship program teaches you to build knowledge-aware AI: retrieval-augmented generation, vector databases, knowledge graphs and memory. The running example is Smart.edu-gate.pro itself — every course here has its own knowledge space that the Smart Instructor searches before it answers.

Prerequisites: Python basics and Generative AI Foundations.

Skills you will build
Retrieval-Augmented GenerationKnowledge Graphs & AI MemoryGenerative AIPython Programming

Final project (AI Lab)

Build a Smart Course RAG system: ingest a course (lessons + PDFs), chunk and index it with hybrid search, answer learner questions with citations and a confidence score, refuse when evidence is missing, and report retrieval recall@5 and groundedness on a 20-question evaluation set.

Built in the AI Lab with your Smart Instructor, Coding, Debugging, Tutor and Assessment experts — evaluated against a rubric and reviewed by your trainer.

Your trainer

S
Dr. Sara Al-Mansouri
Artificial Intelligence
AI Researcher & Educator · Smart AI Academy lead

Sara leads applied AI programs and has trained thousands of engineers in machine learning, generative AI, RAG and multi-expert systems.

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  • 5.5 hours of content
  • Smart Instructor with cited answers
  • AI-generated practice & adaptive remediation
  • Verifiable certificate of completion
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AI Engineer