RIO World AI Hub

Standards for Generative AI Interoperability: APIs, Formats, and LLMOps

MCP is the new standard for generative AI interoperability, enabling seamless tool integration across vendors. Learn how APIs, formats, and LLMOps are converging to make enterprise AI scalable and compliant.

How to Prevent Sensitive Prompt and System Prompt Leakage in LLMs

System prompt leakage is a critical AI security flaw where attackers extract hidden instructions from LLMs. Learn how to prevent it with proven strategies like prompt separation, output filtering, and external guardrails - backed by 2025 research and real-world cases.

Key Components of Large Language Models: Embeddings, Attention, and Feedforward Networks Explained

Understand the three core parts of large language models: embeddings that turn words into numbers, attention that connects them, and feedforward networks that turn connections into understanding. No jargon, just clarity.

Vibe Coding Adoption Roadmap: From Pilot Projects to Broad Rollout

Vibe coding lets anyone turn plain language into working apps-but only if you start small, refine with humans, and scale with rules. Learn the real roadmap from pilot to rollout.

Operating Model for LLM Adoption: Teams, Roles, and Responsibilities

A clear operating model for LLM adoption defines teams, roles, and responsibilities to avoid costly failures. Learn the essential roles like prompt engineers and LLM evaluators, how to structure cross-functional teams, and why most LLM projects fail due to organizational gaps-not technical ones.

Tool Use with Large Language Models: Function Calling and External APIs

Function calling lets large language models interact with real-time data and external tools using structured JSON requests. Learn how it works, how major models differ, where it shines, and what pitfalls to avoid.