Retrieval-Augmented Generation Engineering Training Course

Retrieval-Augmented Generation Engineering Training Course

Introduction

Retrieval-Augmented Generation (RAG) Engineering Training Course provides comprehensive expertise in designing, developing, and deploying enterprise-grade AI knowledge systems that combine Large Language Models (LLMs) with advanced information retrieval, vector databases, semantic search, and knowledge augmentation techniques. As organizations increasingly adopt Generative AI, AI Agents, Enterprise Search, and Intelligent Automation, RAG engineering has become a critical capability for building reliable, context-aware, and scalable AI applications. This course explores modern RAG architectures, embedding models, vector indexing, document intelligence, retrieval optimization, prompt engineering, LLM orchestration, and production AI deployment strategies.

Participants will gain practical skills in building high-performance AI assistants, domain-specific copilots, conversational search platforms, and knowledge management solutions using cutting-edge RAG frameworks and technologies. Through hands-on labs, real-world case studies, and industry scenarios, learners will master techniques for improving AI accuracy, reducing hallucinations, enhancing contextual understanding, and delivering trustworthy enterprise AI solutions. The course prepares professionals to engineer next-generation AI-powered applications using scalable retrieval pipelines, hybrid search, agentic workflows, and responsible AI practices.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand Retrieval-Augmented Generation architectures and enterprise AI design principles. 
2. Build scalable RAG pipelines using LLMs, embeddings, and vector databases. 
3. Implement advanced semantic search and knowledge retrieval strategies. 
4. Design optimized document ingestion and preprocessing workflows. 
5. Develop production-ready AI assistants and enterprise copilots. 
6. Apply prompt engineering and context optimization techniques for RAG systems. 
7. Configure and manage vector databases and embedding technologies. 
8. Improve AI reliability through retrieval evaluation and hallucination reduction methods. 
9. Integrate hybrid search, reranking models, and advanced retrieval algorithms. 
10. Deploy secure and scalable cloud-based RAG applications. 
11. Implement AI governance, security, and responsible AI frameworks. 
12. Develop domain-specific knowledge-enhanced AI applications. 
13. Optimize RAG systems for performance, accuracy, scalability, and business impact. 

Target Audience

1. AI Engineers and Machine Learning Engineers 
2. Data Scientists and Data Engineers 
3. Software Developers building Generative AI applications 
4. Cloud Architects and Solution Architects 
5. Enterprise AI and Digital Transformation Teams 
6. NLP Engineers and LLM Application Developers 
7. Knowledge Management and Search Specialists 
8. Technology Leaders and AI Product Managers 

Course Modules

Module 1: Foundations of Retrieval-Augmented Generation (RAG)

• Introduction to RAG architecture and Generative AI ecosystems
• Evolution from traditional search to AI-powered knowledge retrieval
• Components of RAG systems
• Understanding LLMs, context windows, and knowledge grounding
• Designing enterprise RAG solution architectures 
• Case Study: Enterprise AI Knowledge Assistant

Module 2: Data Preparation and Document Intelligence for RAG

• Document collection, cleaning, and preprocessing strategies 
• Text extraction from PDFs, websites, databases, and enterprise systems 
• Document chunking strategies for optimal retrieval performance 
• Metadata management and knowledge organization 
• Building scalable data ingestion pipelines 
• Case Study: Healthcare Knowledge Platform

Module 3: Embeddings and Vector Database Engineering

• Understanding embedding models and semantic representations 
• Selecting and optimizing embedding models 
• Vector indexing and similarity search techniques 
• Working with vector databases and storage architectures 
• Managing large-scale enterprise knowledge repositories 
• Case Study: Customer Support AI System

Module 4: Advanced Retrieval Techniques

• Semantic search and keyword-based retrieval integration 
• Hybrid retrieval architectures 
• Query expansion and query rewriting techniques 
• Retrieval ranking and reranking models 
• Improving precision and recall in RAG systems 
• Case Study: Financial Research Copilot

Module 5: LLM Integration and Prompt Engineering for RAG

• Connecting RAG pipelines with Large Language Models 
• Context engineering and prompt optimization 
• Managing token limitations and context windows 
• Reducing hallucinations through grounding techniques 
• Designing effective RAG prompts and workflows 
• Case Study: Legal AI Assistant

Module 6: RAG Application Development and AI Agents

• Building conversational RAG applications 
• Integrating RAG with AI agents and automation workflows 
• Multi-step reasoning and tool-using AI systems 
• Memory management in AI applications 
• Developing enterprise copilots and intelligent assistants 
• Case Study: Enterprise Productivity Copilot

Module 7: RAG Evaluation, Optimization, and Security

• Measuring retrieval quality and response accuracy 
• RAG evaluation frameworks and benchmarking methods 
• Detecting and reducing AI hallucinations 
• Security considerations for enterprise RAG systems 
• Privacy, governance, and responsible AI implementation 
• Case Study: Banking AI Platform

Module 8: Production Deployment and Enterprise RAG Scaling

• Deploying RAG systems in cloud environments 
• Building scalable AI infrastructure 
• Monitoring RAG performance and user interactions 
• Cost optimization and latency reduction strategies 
• Enterprise integration with APIs and business platforms 
• Case Study: Global Enterprise Search Platform

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.