RIO World AI Hub - Page 3
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.
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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.
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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.
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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.
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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.
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