Understanding RAG & Multi-Agent GenAI: 101

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.

Transcript

1. Understanding RAG & Multi-Agent GenAI: 101 KAMPALA Ronnie Atuhaire GDG Lead MUK, Computer Scientist
2. 1.What’s RAG? 2.RAG Use Cases 3...Vs..Fine Tuning 4.MultiGenAI 5.A Blend 6.Benefits & Challenges 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. 80% GenAI 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.
4. KAMPALA ‘Is it raining in the capital of Wakanda?’ ‘What’s 5 times the temperature of the sun now?’ Rationale
5. 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.
6. …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. Eg Llama Index, Azure AI Search, 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.
7. RAG VISUALISED
8. Why RAG Matters The Why?: • Addresses the limitations of traditional language models constrained by static training data (out-of-date training data).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.
9. KAMPALA ‘What is Willow from Google?’ Hallucination
10. 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.
11. 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. Tools: Llama, Watson X, Sonnet, Hugging Face, Sora, PaliGemma 2, LangSmith
12. …Multi-Agent Generative AI A way to visualise how multi-agent systems can be combined, being flexible to perform simple to more complex tasks.
13. 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
14. RAG Vs Fine-Tuning Vs Embedding
15. Modern Multi-Agent System Example: KAMPALA
16. What If AI Had Superpowers? KAMPALA
17. 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…..
18. ( 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
19. Q&A Thank You! Scan To Connect