Feb. 21, 2026
Understanding Retrieval-Augmented Generation (RAG): Building Grounded, Reliable AI
Unlock the secrets of Retrieval-Augmented Generation (RAG) and discover how this groundbreaking approach is transforming the world of AI! In this presentation, we explore the evolution of RAG—from its origins at NeurIPS 2020 to its role as the backbone of trustworthy, explainable AI. Learn how RAG bridges the gap between memorized knowledge and real-time information, enabling large language models to deliver accurate, up-to-date, and cited answers.
We’ll break down the anatomy of a RAG system, explain key concepts like indexes and embeddings, and showcase practical use cases across enterprise knowledge bases, finance, healthcare, legal, and technical domains. Dive into advanced techniques such as query decomposition, re-ranking, adaptive pipelines, and best practices for deployment, governance, and evaluation.
Whether you’re an AI enthusiast, developer, or business leader, this session will equip you with the knowledge to master RAG and build reliable, scalable AI solutions for the future.
Key Topics Covered:
What is RAG and why it matters
Closed-book vs. open-book AI
Anatomy and components of RAG systems
Types of retrieval: keyword, semantic, hybrid
Practical use cases and deployment scenarios
Advanced techniques: chunking, multi-hop, re-ranking
Governance, security, and evaluation frameworks
Emerging trends and the future of RAG
Call to Action (CTA):
If you found this presentation helpful, don’t forget to like, subscribe, and share! Leave your questions and thoughts in the comments below—let’s build a community of grounded, reliable AI together.
Tags:
Retrieval-Augmented Generation, RAG, AI, Artificial Intelligence, Large Language Models, LLM, Machine Learning, Knowledge Base, Semantic Search, Embeddings, Indexes, Open-Book AI, Enterprise AI, Data Governance, AI Deployment, NeurIPS, Microsoft Foundry, AWS RAG, Explainable AI, Reliable AI, Advanced AI Techniques, Query Decomposition, Re-ranking, Adaptive Retrieval, AI Trends
Hashtags:
#RAG #ArtificialIntelligence #AI #MachineLearning #LLM #KnowledgeBase #SemanticSearch #Embeddings #ExplainableAI #ReliableAI #AIEcosystem #EnterpriseAI #DataGovernance #NeurIPS #MicrosoftFoundry #AWSRAG #FutureOfAI
We’ll break down the anatomy of a RAG system, explain key concepts like indexes and embeddings, and showcase practical use cases across enterprise knowledge bases, finance, healthcare, legal, and technical domains. Dive into advanced techniques such as query decomposition, re-ranking, adaptive pipelines, and best practices for deployment, governance, and evaluation.
Whether you’re an AI enthusiast, developer, or business leader, this session will equip you with the knowledge to master RAG and build reliable, scalable AI solutions for the future.
Key Topics Covered:
What is RAG and why it matters
Closed-book vs. open-book AI
Anatomy and components of RAG systems
Types of retrieval: keyword, semantic, hybrid
Practical use cases and deployment scenarios
Advanced techniques: chunking, multi-hop, re-ranking
Governance, security, and evaluation frameworks
Emerging trends and the future of RAG
Call to Action (CTA):
If you found this presentation helpful, don’t forget to like, subscribe, and share! Leave your questions and thoughts in the comments below—let’s build a community of grounded, reliable AI together.
Tags:
Retrieval-Augmented Generation, RAG, AI, Artificial Intelligence, Large Language Models, LLM, Machine Learning, Knowledge Base, Semantic Search, Embeddings, Indexes, Open-Book AI, Enterprise AI, Data Governance, AI Deployment, NeurIPS, Microsoft Foundry, AWS RAG, Explainable AI, Reliable AI, Advanced AI Techniques, Query Decomposition, Re-ranking, Adaptive Retrieval, AI Trends
Hashtags:
#RAG #ArtificialIntelligence #AI #MachineLearning #LLM #KnowledgeBase #SemanticSearch #Embeddings #ExplainableAI #ReliableAI #AIEcosystem #EnterpriseAI #DataGovernance #NeurIPS #MicrosoftFoundry #AWSRAG #FutureOfAI
