Samia Khalid

In NVIDIA’s Q2 earnings this week, Jensen Huang highlighted physical AI—alongside reasoning AI—as a key growth driver. Analysts echoed this, noting breakthroughs in robotics and autonomous systems as the next stage of AI adoption. It’s a welcome acknowledgment that AI’s impact won’t be confined to digital spaces. The real opportunity lies in the physical world—transportation, logistics, energy, construction—where safety, efficiency, and sustainability gains can be profound. That’s the frontier many of us are working on: bringing AI to the systems and workers that keep the real economy moving.

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How well are LLMs doing in medical applications? Stanford's Human-Centered AI (HAI) institute benchmarked 6 models for some common medical use cases. While there is no clear winner across the board, bigger seems better with GTP-4o having the best results across most applications - while also coming in last at a few ones (like Documenting Diagnostic Reports or Recording Research Processes). Or as the authors put it: "Small models, while adequate for well-structured tasks, struggled with tasks requiring domain expertise, particularly in mental health counseling and medical knowledge assessment. Notably, open-ended text generation produced comparable BertScore-F1 ranges across model sizes." What's your take on these results?

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🚀 Just wrapped up a hands-on project applying Retrieval-Augmented Generation (RAG) to a real-world use case: interpreting LIC’s New Jeevan Shanti policy document using LLMs. The core idea? Combine vector search over regulatory PDFs with GPT-4 to generate grounded, faithful answers to complex policy questions. Key capabilities we built: ✅ LCEL-based RAG architecture using LangChain ✅ Contextual compression retriever for focused document lookup ✅ Modular LLM & embedding selection (OpenAI, HuggingFace, Mistral) ✅ Evaluation using LLM-powered metrics: faithfulness, relevance, correctness 📄 The document used is publicly available here: LIC New Jeevan Shanti Policy PDF 💻 Full code available on GitHub: https://lnkd.in/gS9mEzWA 💡 This architecture brings the best of both worlds — retrieval accuracy and generative flexibility — especially in regulated domains like insurance and financial services. 🔜 Next step: Incorporate Agents that can reason across multiple steps — not just answer a question, but decide which tools to use, when to search, how to validate, and when to stop. That’s where the future of enterprise GenAI lies. Would love to hear how others are pushing RAG, agents, and LLMs in production! #GenAI #LangChain #LLM #RAG #AIForOps #InsuranceTech #LLMEvaluation #LangChainAgents

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The emergent multi-agent applications rely on sophisticated language models and often need to interact with other systems, built on other frameworks and crossing organizational boundaries. Two distinct protocols play critical roles in enabling these capabilities: Anthropic’s Model Context Protocol (MCP) and our recently announced Google Agent-to-Agent (A2A) interoperability protocol. Both relate to agentic AI, but, they address different levels and aspects of agentic system design. During our Google NEXT 2025 conference, after we annoucned this, as an author of this protocol, I was asked many questions by our customers and partners and academics. One of the most salient questions were around how these protocols compare and contrast. This article aims to clarify their specific functions, differences, and how they can complement each other. Let me know if you have any questions. https://lnkd.in/gKbQa8sM #A2A #AgenticAI #Agents #MultiAgentSystems #EnterpriseAI Google Cloud #Agent2Agent #Interoperability #GenAI

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