Why Context Engineering is the new AI operating system

Choosing the right AI Agent framework can feel overwhelming. Should you go experimental (AutoGPT, BabyAGI) or production-ready (LangChain, Rasa, LlamaIndex)? It really comes down to your primary focus: 🔄 Orchestration 💬 Chatbots 📊 Data & Retrieval The landscape is evolving FAST—agentic AI is no longer just a buzzword; it’s shaping how teams build, deploy, and scale automation. 👉 Which framework are you betting on for 2025? #AI #AgenticAI #RAG #LangChain #AutoGPT #ArtificialIntelligence #FutureOfWork

As AI grows, so does the need for designers to speak the language of data. Here’s the catch: → With AI automating processes, designs are no longer just static. → You can vibe-code them and make it work. → This is where you need to understand what JSON is I’ve broken down JSON for designers in this no-fluff carousel because in this AI-driven world, knowing how to read and use data isn’t optional. Save it for later, it’ll make you a smarter designer. P.S. AI is here, JSON is how we talk to it. Ready to vibe-code your way to the future of design?

What we’ve learned building real-world AI products After working across multiple enterprise AI deployments, one insight has become painfully clear: Specificity = Trust. Specific prompts → specific responses → specific user value. This is not a “fine-tuning” problem. It’s a product design problem. The onus is on product teams to shape AI outputs that feel tailored, useful, and human. ⸻ For builders and product leaders: 💡 Start here: • Build for narrow workflows, not broad topics. • Inject relevant context at every step—user role, metadata, previous actions. • Write and test prompts like you write UI copy: intentionally, repeatedly. • Don’t let your “default” be “generic.” ⸻ AI works. But only when we stop treating it like magic—and start treating it like product. ⸻ Would love to hear from others building real AI systems: What’s your favorite way to inject specificity?

recently came across these 4 incredible Generative AI repositories from Nir Diamant, and I can't believe I didn't discover them earlier. For anyone looking to learn GenAI for free with actual working code, these are outstanding resources. 💬 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 https://lnkd.in/g759UvtJ Master the art of communicating with AI through 22 comprehensive notebook tutorials. From foundational concepts like zero-shot and few-shot learning to advanced strategies including Chain of Thought reasoning and self-consistency. 📤 𝗥𝗔𝗚 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 https://lnkd.in/gw6yE_Ws Discover 30+ cutting-edge methods for building Retrieval-Augmented Generation systems that deliver accurate, context-rich responses. This collection covers everything from basic chunking strategies to sophisticated approaches like Graph RAG, RAPTOR, and multi-modal retrieval. 🤖 𝗚𝗲𝗻𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 https://lnkd.in/gMFVna2j Build intelligent AI agents from scratch with 40+ practical implementations ranging from simple chatbots to complex multi-agent systems. Each tutorial demonstrates real-world applications including customer support, content creation, research automation, and game development. 🚀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗼𝘄𝗮𝗿𝗱𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 https://lnkd.in/gCpq2KbN Bridge the gap between prototype and production with patterns for deploying AI agents at scale. This course covers the entire production lifecycle including memory management, observability and monitoring, security guardrails, multi-agent coordination, automated testing, and deployment strategies. Happy Learning 📚 📚 📚

I recently came across these 4 incredible Generative AI repositories from Nir Diamant, and I can't believe I didn't discover them earlier. For anyone looking to learn GenAI for free with actual working code, these are outstanding resources. 💬 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 https://lnkd.in/g759UvtJ Master the art of communicating with AI through 22 comprehensive notebook tutorials. From foundational concepts like zero-shot and few-shot learning to advanced strategies including Chain of Thought reasoning and self-consistency. 📤 𝗥𝗔𝗚 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 https://lnkd.in/gw6yE_Ws Discover 30+ cutting-edge methods for building Retrieval-Augmented Generation systems that deliver accurate, context-rich responses. This collection covers everything from basic chunking strategies to sophisticated approaches like Graph RAG, RAPTOR, and multi-modal retrieval. 🤖 𝗚𝗲𝗻𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 https://lnkd.in/gMFVna2j Build intelligent AI agents from scratch with 40+ practical implementations ranging from simple chatbots to complex multi-agent systems. Each tutorial demonstrates real-world applications including customer support, content creation, research automation, and game development. 🚀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗼𝘄𝗮𝗿𝗱𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 https://lnkd.in/gCpq2KbN Bridge the gap between prototype and production with patterns for deploying AI agents at scale. This course covers the entire production lifecycle including memory management, observability and monitoring, security guardrails, multi-agent coordination, automated testing, and deployment strategies. Happy Learning 📚 📚 📚

Most people think building AI agents is easy. But not the kind that actually work. Anyone can spin up a chatbot. Building AI Workers that think, listen, speak, remember, and improve over time is a whole different game. These are the seven videos that show what really goes into it. This is the level we operate at inside The AI Studio. How LLMs Actually Work Yannic Kilcher breaks down what powers models like GPT-4 and Claude 🔗 https://lnkd.in/dgiudQNF LLMs from Scratch The deep dive into transformers, architecture, and training 🔗 https://lnkd.in/dsVsv3Xy Stanford on Agentic AI Why AI agents matter and how they’re reshaping the stack 🔗 https://lnkd.in/dW8XUQKM How to Build and Evaluate AI Agents Testing frameworks most builders skip 🔗 https://lnkd.in/dmWWHkiK Building Real-World Agents What it takes to get agents delivering outcomes 🔗 https://lnkd.in/dfhvfhtD Microsoft’s MCP Framework The orchestration layer behind serious agent systems 🔗 https://lnkd.in/dKBY3aCn Agents from Scratch (Hands-on) A step-by-step look at how the full stack comes together 🔗 https://lnkd.in/dXNNWEaQ If you want to understand what it takes to build AI that actually does the work, start here — or drop me a DM and let’s have a chat.

AI frameworks promise fast agents without understanding how they work. Here's the catch... 🤔 AI frameworks today sell a tempting promise: "Build agents fast, no deep knowledge required!" Great for prototypes and impressing stakeholders. But here's what happens next: Production accuracy requirements hit. Complex functionality needs emerge. Access control becomes critical. Suddenly, the framework starts working against you instead of with you. The reality? There's still a place for frameworks - especially for smaller teams or when MVP speed is everything. But for complex cases, building from scratch often wins. Just wrote a full breakdown of the options teams should consider when building agents - covering when frameworks make sense, when they don't, and everything in between. What's been your experience - frameworks helping or hurting in production?

🚀 Navigating AI Model Customization – A Simple Guide One of the most common questions I get asked: 👉 “How do I decide whether my AI model needs prompt engineering, RAG, fine-tuning, or something more advanced?” The truth is – there’s no single answer. It depends on your data, your goals, and your constraints. That’s why I created this visual decision flowchart to help teams navigate the AI model customization journey. This framework guides you step by step:  • Do you need real-time grounding with external data?  • Is your training data stable and large enough for tuning?  • Is your desired output easy or hard to define with examples?  • Do you want speed, cost efficiency, or deep customization? 💡 By following this path, you’ll know whether Prompt Engineering, RAG, RLHF, SLM, Fine-tuning, or PEFT/SFT is right for your use case. 🔑 Quick glossary of terms (simple one-liners):  • MCP (Model Context Protocol): Lets AI access external tools, APIs, and data in real-time.  • RAG (Retrieval-Augmented Generation): Connects AI with external knowledge bases.  • RLHF (Reinforcement Learning with Human Feedback): Improves AI by learning from human ratings and corrections.  • SLM (Small Language Model): A lighter, faster, cost-efficient AI model with focused knowledge.  • PEFT (Parameter-Efficient Fine-Tuning): Tunes only part of a model to customize it quickly and cheaply.  • SFT (Supervised Fine-Tuning): Trains AI with labeled examples to align with specific tasks. ✨ AI isn’t about “one-size-fits-all.” Choosing the right customization path can save you time, cost, and effort while unlocking the impact you want. What’s your current approach – Prompt Engineering, RAG, or Fine-tuning? #AI #MachineLearning #GenerativeAI #LLM #FineTuning #PromptEngineering #RAG #AIwithSudheer

*Unlocking the Power of AI for Technical Research* 💡 In today's fast-paced world, getting accurate, detailed information quickly is a game-changer. Here is a deep research agent I designed to automate and enhance the technical report writing process. In this Demo i have asked the agent to do a Deep research on "Advancement in Ai ". I Also have attached the Report generated by the Model in the First comment This agent doesn't just summarize information; it acts as a true research partner, handling everything from strategic planning to final report generation. Here's a quick look at how it works: Intelligent Report Planning: The agent dynamically plans a comprehensive report by first generating a detailed outline and targeted search queries based on the user's topic. Web-Powered Insights: It leverages live web searches to gather the most current and relevant data, ensuring the information is fresh and authoritative. Structured Content Creation: Each section of the report is written independently, focusing on a specific sub-topic with technical accuracy, citations, and concrete examples. Seamless Assembly: Finally, it compiles all the drafted sections—including the introduction and conclusion—into a single, polished, and properly formatted Markdown report. This project demonstrates the potential of AI to not only assist but also to lead complex tasks, freeing up valuable time for deeper analysis and creative problem-solving. It's a testament to how we can build more powerful, autonomous systems with a clear and structured approach. Let me know what you think in the comments! 👇

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝟴 𝗴𝘂𝗶𝗱𝗲𝘀 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Tools and new AI models will keep changing. The fundamentals of working with them won’t. This is some of the best material you can study right now.+ 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗺𝗶𝘀𝘀: ⬇️ 1. Prompt Engineering → How to design inputs that unlock high-quality outputs: https://lnkd.in/gc-VBwiD 2. Claude Code: Best practices for agentic coding → How to structure code so agents can read, edit, and extend it reliably: https://lnkd.in/gK_S2UtY 3. Building Effective Agents → How to design, evaluate, and run AI agents in practice: https://lnkd.in/giyHGjxi 4. The AI Fluency Framework → A model to understand and scale AI adoption across teams: https://lnkd.in/gBwbzBGy 5. Build with Claude → API fundamentals and best practices to ship production apps with Claude: https://lnkd.in/geA7JSXr 6. Claude for Work → Practical ways to implement Claude to boost team productivity: https://lnkd.in/gHUpsxSp 7. Anthropic API Fundamentals → Learn the core concepts to call and integrate Claude: https://lnkd.in/gKtgSBjk 8. Real World Prompting → Practical prompting techniques from real enterprise use cases: https://lnkd.in/gzvYMT3e AI is moving fast. The best way to stay ahead is to learn directly from the people building the frontier models.