A complete development workflow suite covering the full cycle: brainstorm → plan → execute → TDD → debug → code review → verify → ship. 14 skills working together. Triggers: writing code, fixing bugs, ...
Six months ago, I spent two weeks building a “smart” customer support agent. It could answer questions, look up order status, and even process refunds. I was proud of it. The integration code was a ...
Python MCP Servers make it easy to connect Large Language Models (LLMs) securely with real-world data and tools. The Model Context Protocol standardizes safe, efficient communication between AI models ...
Written by Ken Huang, CSA Fellow, Co-Chair of CSA AI Safety Working Groups and Dr. Ying-Jung Chen, Georgia Institute of Technology. This implementation guide provides a comprehensive, hands-on ...
HANDS ON Getting large language models to actually do something useful usually means wiring them up to external data, tools, or APIs. The trouble is, there's no standard way to do that - yet.
In this tutorial, you will learn how to build a full-featured Retrieval-Augmented Generation (RAG) server using IBM Watsonx.ai, ChromaDB for vector indexing, and expose it via the Model Context ...
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