LLM Grounding with Enterprise Data (RAG Techniques)

Improve accuracy by grounding GenAI outputs in internal data sources.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course focuses on Retrieval-Augmented Generation (RAG) techniques to enhance Large Language Models (LLMs) by integrating enterprise-specific data. Participants will learn how to connect GenAI models with internal knowledge bases, databases, and documents to produce more accurate, relevant, and trustworthy AI-generated responses aligned with organizational context.

Learning Objectives

Participants will be able to:

• Understand the concept and benefits of RAG for LLMs

• Implement data retrieval strategies that connect LLMs to enterprise data

• Design workflows that integrate external and internal data sources

• Evaluate the impact of grounding on AI output accuracy and relevance

• Manage data privacy, security, and governance when using RAG

Target Audience

• Data engineers and AI developers

• Knowledge management professionals

• Enterprise architects and solution designers

• AI project and product managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures on RAG architecture and use cases

• Practical labs connecting LLMs to enterprise datasets

• Group exercises on query design and system tuning

• Discussions on compliance and ethical considerations

Materials Provided

• Sample enterprise datasets and knowledge bases

• Code snippets and integration templates for RAG workflows

• Guidelines on data governance and security

• Assessment tools for output quality and relevance

Outcomes

• Hands-on experience implementing RAG to ground LLM outputs

• Ability to design enterprise AI systems leveraging internal data

• Improved understanding of accuracy and trustworthiness in GenAI

• Awareness of governance challenges with internal data integration

Outline / Content

Day 1: Introduction to RAG and LLM Grounding Concepts

• Fundamentals of Retrieval-Augmented Generation

• Why grounding LLMs with enterprise data matters

Day 2: Connecting LLMs to Enterprise Data Sources

• Techniques for indexing, querying, and retrieving internal data

• Integration architectures and tools

Day 3: Building and Testing RAG Workflows

• Hands-on exercises with sample datasets

• Optimizing prompt design for grounded generation

Day 4: Governance, Security, and Performance Monitoring

• Ensuring data privacy and compliance

• Monitoring output quality and user feedback loops