Events

1. AI & LLMs: The Foundation
• The journey of AI: From basic rule-based systems to modern deep learning models.
• What are LLMs (Large Language Models), and how do they create human-like responses?
• Key limitations: Limited memory, risks of making up information ("hallucinations"), and relying on outdated knowledge.
2. RAG: Why Do We Need It?
• The challenge: LLMs can't know or store everything.
• The solution: Combining generative AI with real-world information retrieval.
• Think of RAG as: "An AI with access to an open book."
3. How RAG Works: A Simple Overview
• Retriever: Finds the relevant information from a database or the web.
• Generator: Uses the retrieved information to create accurate, helpful responses.
• Easy analogy: "RAG is like combining ChatGPT with Google Search."
4. Where RAG is Used (Real-World Applications)
• Smarter virtual assistants like Bing Chat or Perplexity AI.
• Customer support chatbots that pull data from real company databases.
• Automating tasks in fields like law, healthcare, and academic research.
5. Hands-on Demo: Building a Mini-RAG System
• Add knowledge (like PDFs, text, or web data).
• Use embedding models to convert text into searchable formats.
• Save and search data using tools like FAISS or Pinecone.
• Test a query to see how RAG enhances the results.
6. Challenges & Future of RAG
• Handling retrieval errors and reducing biases in retrieved data.
• Improving speed and scalability for better performance.
• The future: Smarter systems with memory-like features and multi-modal capabilities (text, images, and more).
7. Open Discussion & Q&A
• Practical advice for incorporating RAG into real-world projects.
• Exploring open-source tools like LangChain and LlamaIndex.
• Predictions for the next phase of AI in intelligent searches.

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