Episode 2 - AI Design Patterns Prompting, RAG & Agents

Description

Explore advanced AI design patterns including Prompt Engineering, Retrieval-Augmented Generation (RAG), and Autonomous Agents. We dive into Model Context Protocol (MCP) and how to manage AI memory for scalable business solutions. *Content Highlights:* * Prompt Engineering: Role prompting, structure, and chain of thought. * RAG (Retrieval-Augmented Generation) and Vector Search architecture. * AI Memory management: Short-term vs. Long-term persistence. * The role of Tools (Function Calling) and Autonomous Agents. * Introduction to the Model Context Protocol (MCP) by Anthropic. 🌐 AI Platform & Library: https://ai.boxlang.io 📘 Documentation: https://ai.ortusbooks.com 🐙 Source Code: https://github.com/ortus-boxlang/bx-ai ──────────────────────────────── 🚀 What is BoxLang? BoxLang is a modern, dynamic JVM language built for productivity, performance, and flexibility. It runs natively on the JVM, supports scripting and compiled execution, integrates seamlessly with Java libraries, and powers applications across: 🌐 Web ☁️ Serverless 🧪 Testing 🖥️ CLI 🧠 AI & Agent systems 🖱️ Desktop applications BoxLang is designed to let developers move fast—without sacrificing structure, performance, or control. 🌍 Learn more: https://www.boxlang.io 🐙 GitHub: https://github.com/ortus-boxlang/BoxLang ──────────────────────────────── 🔗 Resources & Community 🧠 BoxLang AI Library: https://ai.boxlang.io 📘 BoxLang AI Docs: https://ai.ortusbooks.com 🌍 BoxLang Main Site: https://www.boxlang.io ❌ X (Twitter): https://x.com/TryBoxLang 🐙 GitHub: https://github.com/ortus-boxlang/BoxLang 📦 bx-ai Repo: https://github.com/ortus-boxlang/bx-ai 📘 Facebook: https://www.facebook.com/tryboxlang/ 💼 LinkedIn: https://www.linkedin.com/company/103849045/ 🧵 Threads: https://www.threads.com/@ortussolutions 👍 Like, subscribe, and follow BoxLang to stay up to date on AI, agents, and JVM-powered innovation.