Benefits of Context-Aware AI

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗼𝘂𝗹𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘁𝗼𝗼𝗹𝘀 𝗷𝘂𝘀𝘁 𝗹𝗶𝗸𝗲 𝗵𝘂𝗺𝗮𝗻𝘀 — 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀, 𝗮𝗻𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴? That's the promise of 𝗠𝗖𝗣 — 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 by Anthropic. And here’s how it’s transforming how AI agents fetch stock market data- Let’s say a user asks: “𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝘀𝘁𝗼𝗰𝗸 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗔𝗽𝗽𝗹𝗲 𝗜𝗻𝗰.?” With 𝗠𝗖𝗣, here’s what actually happens under the hood: 1. The 𝗟𝗟𝗠 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘁𝗵𝗲 𝗾𝘂𝗲𝗿𝘆 and routes it through the MCP Client. 2. It uses 𝘁𝗼𝗼𝗹𝘀, like a financial data API, in a structured and secure way. 3. The system checks for 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗿𝘂𝗹𝗲𝘀 (like whether financial APIs are allowed). 4. Once approved, the request reaches a stock API via the MCP Server. 5. The LLM receives fresh, real-time data — and responds intelligently, as if it “knows” the answer.     This is not just about calling APIs. It’s about giving AI models a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹-𝘂𝘀𝗲 𝗹𝗮𝘆𝗲𝗿 — bridging natural language with enterprise-grade execution. With MCP, LLMs don’t just 𝘨𝘶𝘦𝘴𝘴 — they act like agents with 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀, governed by 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗹𝗼𝗴𝗶𝗰. This is the direction we’re heading in: AI agents that are reliable, safe, and 𝘵𝘰𝘰𝘭-𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘣𝘺 𝘥𝘦𝘴𝘪𝘨𝘯. What other use cases do you see for MCP in enterprise settings?

The difference between useful AI and expensive noise in healthcare? Context. While most organizations wait for vendor roadmaps, small teams at CHOP and Stanford are solving AI's fundamental challenge: giving LLMs the clinical context they need to actually help patients. CHOP's CHIPPER - A single informaticist used Model Context Protocol to orchestrate 17 clinical tools, creating an AI assistant that understands patient history, current medications, lab trends, and clinical guidelines simultaneously. Development time? Months, not years. Stanford's ChatEHR - Embedded directly in Epic, reducing emergency physician chart review time by 40% during critical handoffs. Built by a small multidisciplinary team focused on workflow integration over feature lists. What makes this significant: → Open frameworks (MCP, SMART-on-FHIR) enable rapid innovation → Small teams with hybrid expertise move faster than large vendor projects → Context matters more than AI model capabilities → Workflow integration beats standalone AI applications The organizations building clinical context infrastructure today will have significant advantages as AI capabilities mature. #HealthcareIT #ArtificialIntelligence #ClinicalInformatics #HealthTech This non-AI-generated image is a real scene from my life. Visited with family last week and welcomed our first grandchild. Not the dog, a real grandchild, but I'm not at liberty to share pictures just yet.

Prompting tells AI what to do. But Context Engineering tells it what to think about. Therefore, AI systems can interpret, retain, and apply relevant information dynamically, leading to more accurate and personalized outputs. You’ve probably started hearing this term floating around a lot lately, but haven’t had the time to look deep into it. This quick guide can help shed some light. 🔸What Is Context Engineering? It’s the art of structuring everything an AI needs not just prompts, but memory, tools, system instructions, and more to generate intelligent responses across sessions. 🔸How It Works You give input, and the system layers on context like past interactions, metadata, and external tools before packaging it into a single prompt. The result? Smarter, more useful outputs. 🔸Key Components From system instructions and session memory to RAG pipelines and long-term memory, context engineering pulls in all these parts to guide LLM behavior more precisely. 🔸Why It’s Better Than Prompting Alone Prompt engineering is just about crafting the right words. Context engineering is about building the full ecosystem, including memory, tool use, reasoning, reusability, and seamless UX. 🔸Tools Making It Possible LangChain, LlamaIndex, and CrewAI handle multi-step reasoning. Vector DBs and MCP enable structured data flow. ReAct and Function Calling APIs activate tools inside context. 🔸Why It Matters Now Context engineering is what makes AI agents reliable, adaptive, and capable of deep reasoning. It’s the next leap after prompts, welcome to the intelligence revolution. 🔹🔹Structuring and managing context effectively through memory, retrieval, and system instructions allows AI agents to perform complex, multi-turn tasks with coherence and continuity. Hope this helps clarify a few things on your end. Feel free to share, and follow for more deep dives into RAG, agent frameworks, and AI workflows. #genai #aiagents #artificialintelligence

12 months ago, everyone was talking about prompt engineering. And don't get me wrong - it's still important. But here's what I've learned building Scalafai: GenAI is WAY more powerful when it already knows what you're working on with important details readily available. 🦾 Think about your typical GenAI workflow. You'd write a prompt in ChatGPT/Claude/Groq/etc. to help with a task, then switch between apps to copy/paste the info you need to insert critical context into your prompt. Sound familiar? 🤔 The real breakthrough isn't better prompts - it's giving AI the context it needs automatically. Think about. Any assistant, AI or human, can help you much more when they know: 1. the bigger picture of what you are trying to accomplish 2. the important contextual details needed to finish the task in a way that aligns with the bigger picture We built Scalafai around this fundamental concept. Instead of making people better prompt engineers, we made AI context-aware by connecting it to your existing communications data - emails, chats, meetings transcripts, files, calendars. The result? GenAI becomes incredibly user-friendly and actually useful for real work. When AI already knows your project goals, your team's progress, and the conversations happening across your apps, something magical happens. You can just ask "what's the latest?" and get exactly what you need. Literally. It's the shift from prompt engineering to context engineering. Anyone else tired of the copy-paste dance? 😅 Follow Scalafai for more insights on context-aware AI! #AI #Scalafai #ContextEngineering #PromptEngineering