Learn how to build real-world AI agents using frameworks like LangChain, LangGraph, CrewAI, and AutoGen. This playlist covers agent architectures, workflows, tool calling, memory, and real-world use cases — from beginner to advanced. Perfect for AI engineers, developers, and system design learners. #AIAgents #GenerativeAI #LangChain #AIEngineering
Curated by: Naveen Tech Hub (38 videos)
How do you stop sensitive customer data from reaching a Generative AI model? In this YouTube Short, learn how **Amazon Bedrock Guardrails** can detect and protect Personally Identifiable Information (PII) such as names, email addresses, phone numbers, access keys, and custom enterprise IDs. You’ll quickly understand how Bedrock Guardrails can: 🔒 Detect sensitive PII in AI prompts 🛡️ **ANONYMIZE** data using placeholders such as `{NAME}` and `{EMAIL}` 🚫 **BLOCK** highly sensitive information when needed ⚡ Protect both user prompts and model responses 🧩 Use custom regex patterns for IDs such as `EMP-12345` ☁️ Add an extra privacy and security layer to production GenAI applications **Simple example:** `john@example.com` → `{EMAIL}` Instead of sending raw sensitive information to your AI workflow, Bedrock Guardrails can mask it before processing. This is especially useful for developers building secure **Generative AI, RAG, Agentic AI, and enterprise AI applications on AWS**. Subscribe to **Naveen TechHub** for practical tutorials on Amazon Bedrock, AWS Generative AI, LangChain, RAG, Agentic AI, Python, and cloud architecture. #AmazonBedrock #BedrockGuardrails #GenerativeAI #AWS #GenAI #PII #DataPrivacy #AISecurity #ResponsibleAI #AmazonWebServices #Python #Boto3 #Shorts #naveentechhub