All Posts

Some Large Language Models (LLMs) are vulnerable to security attacks because they treat all instructions equally. Implementing a clear instruction hierarchy—where developer instructions (highest priviledge) override user queries (medium priviledge), which override model outputs (lower priviledge), which override third-party content (lowest priviledge)—significantly improves security and enables more effective prompt engineering. OpenAI's research shows models trained with hierarchical instruction awareness demonstrate up to 63% better resistance to attacks while maintaining functionality. This approach not only mirrors traditional security models in operating systems and organizations, creating more trustworthy AI systems, but also provides prompt engineers with a more predictable framework for crafting reliable prompts that work as intended.

A comprehensive guide to evaluating large language models, covering fundamental metrics, open-ended evaluation techniques, LLM-as-a-Judge approaches, and practical guidance for implementing robust evaluation pipelines in real-world AI applications.

The Model Context Protocol (MCP) is a standardized framework for integrating AI systems with diverse data sources and Applications. This post explores MCP’s architecture, core components, and best practices.

Explore the six essential elements that make multi-agent systems effective: Role Playing, Focus, Tools, Collaboration, Guardrails, and Memory. Learn how specialized agents working together can outperform single-agent solutions through clear roles, focused responsibilities, and powerful collaboration patterns.

Explore ReAct, a framework where language models observe, reason, and act in a continuous cycle. Learn how this three-step process enables AI to gather information, think through problems step-by-step, and take concrete actions - creating more capable and reliable AI systems that can adapt their approach based on real-world feedback.