Understanding RAG and Multi-Agent GenAI: A Gentle Introduction

In this introductory session, we will demystify two groundbreaking technologies: Retrieval-Augmented Generation (RAG) and Multi-Agent Generative AI. Attendees will learn about the fundamentals of these concepts, their significance in modern AI applications, and how they can be leveraged to create intelligent, responsive systems.

Transcript

1. Understanding RAG & Multi-Agent GenAI: A Gentle Introduction MBARARA Ronnie Atuhaire Computer Scientist
2. 1.RAG Defined 2.RAG Use Cases 3.Fine Tuning 4.MultiGenAI 5.A Blend Expectations… Explore Retrieval-Augmented Generation (RAG) and Multi-Agent Generative AI, their integration, benefits, and real-world applications, followed by a Q&A session to deepen your understanding.
3. MBARARA ‘What’s the capital of Wakanda?’ ‘What’s 5 times the temperature of the sun?’ Rationale
4. Introduction to RAG RAG (Retrieval-Augmented Generation) integrates data retrieval with language generation, enabling models to pull real-time, relevant information for more accurate responses. Key Point: Combines the strengths of retrieval systems and generative AI.
5. …RAG Retrieval: • When an AI like ChatGPT doesn’t know something, it usually guesses based on what it’s been trained on. But RAG doesn’t guess blindly. • Instead, it searches external sources (like books, databases, or the internet) for real, up-to-date information. Augmentation: • The retrieved information is added to what the AI already knows. This makes the AI smarter, more accurate, and more reliable. Generation: • Using its language skills, the AI takes the retrieved facts and writes a polished, human-like answer.
6. RAG VISUALISED
7. Why RAG Matters Rationale: • Addresses the limitations of traditional language models constrained by static training data. • Enhances response relevance and accuracy. • Reduce Hallucination & Training Costs Modern Example: • ChatGPT with Browsing Capabilities: Uses real-time web retrieval to answer questions that require up-to-date information.
8. RAG $ On-Device ML Using RAG with on-device ML for data-sensitive applications. Example: Smart assistants that retrieve local data (e.g., notes, contacts) and generate responses without cloud dependency.
9. 80% Gen AI Adoption By 2026, more than 80% of enterprises will have used generative AI APIs and models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023.
10. Introduction to Multi-Agent Generative AI Multi-agent systems involve several AI agents that collaborate, each handling different tasks, to solve problems more effectively. Concept: Agents can specialize, work in parallel, and share insights to optimize task completion.
11. …Multi-Agent Generative AI A way to visualise how multi-agent systems can be combined, being flexible to perform simple to more complex tasks.
12. The journey of a thousand miles begins with a single step — and sometimes, that step is just breaking the problem into smaller steps. Chain Of Thought Prompting
13. Modern Multi-Agent System Example MBARARA
14. What If AI Had Superpowers? MBARARA
15. Benefits of RAG and Multi-Agent Systems Key Advantages: • RAG: More accurate, contextually enriched outputs. • Multi-Agent: Distributed task handling leads to efficiency and robustness. Industry Use Cases: • Healthcare: Retrieval of medical literature for up-to-date clinical decisions. • Finance: Agents working in real-time to gather and interpret financial news for analysis. • +++ More…..
16. ( 1 ) Challenges and Considerations Data Quality: Ensuring the reliability of retrieved information. Solution: Periodic Data Updates ( 2 ) Agent Coordination: Managing agent interactions to avoid conflicts. Solution: Robust coordination protocols. ( 3 ) Expensive Solution: Start Small
17. Q&A Thank You! Scan To Connect