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Docling unlocks enterprise PDFs by converting them into a structured DoclingDocument with layout-aware pipelines, DocTags, and integrations that keep provenance intact for downstream AI and automation workflows.

Use Pydantic as your LLM contract: prompt with the actual schema, validate every boundary (including strict tool-call args), and turn ValidationErrors into structured retries rather than brittle prompt hacks; keep models provider-agnostic via thin wrappers (OpenAI structured outputs, Instructor, Pydantic AI), log like a flight recorder with prompts/model IDs/retry counts/versioned schemas, and evolve safely with explicit schema_version and migration validators, so rogue enums and bad JSON never touch production.

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.

DeepSeek R1 is an open-source LLM that uses reinforcement learning to achieve reasoning capabilities comparable to leading closed models like o1, but at a fraction of the cost. This post explores its novel training approach, benchmarks, and implications for the future of AI reasoning.

Discover the RAG Triad framework - a systematic approach to evaluating RAG systems through three key pillars: context relevance, groundedness, and answer relevance. Learn how this framework helps build trustworthy AI by detecting hallucinations and ensuring responses are reliable and verifiable.

MemGPT revolutionizes LLM capabilities by implementing operating system-like memory management, enabling persistent context and long-term learning across conversations.