RAG and Enterprise Knowledge Systems
A two-day program on building Retrieval-Augmented Generation (RAG) systems that let AI leverage enterprise knowledge securely and accurately.
Course Overview
Retrieval-Augmented Generation (RAG) enables organizations to build AI systems that leverage their own knowledge base securely and accurately. This program covers the full RAG pipeline — from document processing and embedding to retrieval, generation, and evaluation. Participants learn how to build enterprise-grade RAG systems that deliver accurate, sourced answers from organizational data.
Target Audience
AI engineers, data engineers, knowledge managers, technical architects
Prerequisites
GEN-101 or equivalent. Basic programming and data knowledge.
Learning Objectives
- Design and build RAG systems for enterprise knowledge
- Select and configure embedding models and vector databases
- Optimize retrieval strategies for relevance
- Manage document processing and chunking
- Evaluate and improve RAG system quality
Key Modules
- RAG Architecture and Components
- Document Processing and Chunking Strategies
- Embedding Models and Vector Databases
- Retrieval Strategies and Relevance Optimization
- Generation and Citation Management
- Enterprise Knowledge Base Design
- Evaluation and Continuous Improvement
- Case Studies in Enterprise RAG
Learning Outcomes
- A complete RAG system architecture
- Practical skills in document processing and retrieval
- Understanding of vector database selection
- A framework for evaluating RAG quality
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
Request Course Brochure or Corporate Training
Submit your details and our team will contact you with the full course brochure and enrollment options.