AI & Cybersecurity

Currently Playing: Chapter 2: Building your first RAG app

Welcome to Chapter 2 of the AI & Cybersecurity "Building your first RAG app" Learning Series by KK Mookhey! In this hands-on chapter, we move from theory to practice by building our first Retrieval Augmented Generation (RAG) application. Learn how to solve the critical limitations of Large Language Models—hallucinations and lack of private context—by grounding AI responses in your own documents. What You'll Learn: • RAG Fundamentals - How Retrieval Augmented Generation works and why it matters • Two-Phase Architecture - Understanding indexing (one-time) and querying (ongoing) • Hands-On Coding - Building a working RAG app from scratch using Python • Document Processing - Loading, splitting, chunking, and embedding documents • Vector Databases - Using Chroma DB to store and retrieve embeddings • Reducing Hallucination - Temperature settings and prompt engineering techniques • Real-World Testing - Querying PCI DSS compliance documents accurately Key Technologies Covered: • Python Libraries: PDF readers, text splitters, embedding models • Embedding Model: MiniLM L6v2 for vectorization • Vector Database: Chroma DB for persistent storage • LLM Integration: OpenAI API (easily swappable with Anthropic, Gemini, etc.) • Prompt Engineering: Instructing models to avoid hallucination • Chunking Strategy: Optimal chunk sizes and overlap for context preservation Practical Demo Highlights: • Document Used: PCI DSS Standard (397 pages) • Processing Results: 1,045 chunks created from the document • Success Metric 1: RAG correctly responds "I don't know" when information is unavailable (TLS version query) • Success Metric 2: RAG provides precise, accurate answers when information exists (password requirements) What's Next? In the upcoming chapters, we'll explore the security vulnerabilities of RAG applications and demonstrate how to hack and secure these AI-powered chatbots. Code Repository: 📂 Complete code and documentation available in the Google Doc https://docs.google.com/document/d/1NkGZthHxy3QWdkyeDKC_TB2gFOk2H0n-BKZVZjXwq-Q/edit?tab=t.6ypecfaymbgh 💻 Feel free to copy, modify, and improve the code 🎨 Suggested improvements: Add Streamlit UI, integrate with Claude Desktop via MCP, or deploy on Azure AI Foundry About the Instructor: KK Mookhey is a cybersecurity expert with over 25 years of experience. This series provides practical, hands-on training in building and securing AI applications for cybersecurity professionals. Connect with KK on https://www.linkedin.com/in/kkmookhey/ Course Series: This is Chapter 2 of our complete AI & Cybersecurity Learning Series. • Watch Chapter 1: AI & Cybersecurity Introduction - https://www.youtube.com/watch?v=caSd12M5Axk 🔔 Subscribe and enable notifications for upcoming chapters! Build Your Own RAG App: • Use the provided code as a foundation • Create a user-friendly interface with Streamlit • Integrate with your favorite LLM (OpenAI, Anthropic Claude, Google Gemini) • Deploy on various platforms (Azure AI Foundry, OpenAI Playground, Claude Desktop) • Experiment with different embedding models and chunk sizes Join the Conversation: 💬 What would you like to see in the next chapter? 👍 If you found this tutorial helpful, please like and share! 📢 Share this with developers and security professionals in your network 🐛 Encountered any errors? Drop them in the comments for troubleshooting! Network Intelligence - Empowering professionals with practical AI and Cybersecurity skills. Timestamps: 00:02 - Introduction and Recap of LLM Limitations 00:41 - Understanding RAG: What Problem Does It Solve? 02:30 - RAG Architecture: Indexing vs Querying 03:08 - The Indexing Process: Load, Split, Chunk, Embed, Store 04:52 - Code Walkthrough: Ingest and Query Scripts 06:03 - Setting Up Libraries and Dependencies 07:10 - Configuring the LLM and Retrieval Parameters 08:11 - Creating the Prompt Template 09:19 - Practical Demo: Setting Up the Environment 11:02 - Installing Requirements and Running the Code 13:09 - Debugging Common Errors 14:43 - Ingesting the PCI DSS Document (397 pages, 1,045 chunks) 15:22 - Testing the RAG App: TLS Version Query 16:04 - Testing Password Requirements Query 16:37 - Results Analysis and Next Steps #RAG #RetrievalAugmentedGeneration #AIandCybersecurity #Python #MachineLearning #VectorDatabase #ChromaDB #LLM #OpenAI #Cybersecurity #KKMookhey #NetworkIntelligence #AITutorial #CodingTutorial #PCIDSS #EmbeddingModels #PromptEngineering #aiapplications Access the detailed workshop content, including key topics, frameworks, and practical takeaways, here: https://docs.google.com/document/d/1NkGZthHxy3QWdkyeDKC_TB2gFOk2H0n-BKZVZjXwq-Q/edit?tab=t.6ypecfaymbgh