What is LLMops and why is it different from MLOps?

Data is everywhere — but making it usable for Large Language Models is the real challenge. That’s where LlamaIndex (formerly GPT Index) steps in — a powerful framework that connects your custom data sources (like PDFs, databases, or APIs) directly to LLMs in a structured way. Think of it as the bridge between your raw data and your AI assistant’s intelligence. Instead of feeding everything to the model, LlamaIndex helps your app retrieve only the most relevant context, ensuring responses are accurate, grounded, and cost-efficient. It’s one of the key tools powering modern RAG systems, enabling personalized, data-aware AI applications. 💡 Whether you’re building a chatbot, knowledge retrieval system, or internal AI assistant — LlamaIndex helps your LLM understand what matters most. How are you connecting your data to LLMs today? 🔗 Read the full blog here → https://lnkd.in/gZTWPYZN #LlamaIndex #LLMs #RetrievalAugmentedGeneration #RAG #AIApplications #GenAI #DataToIntelligence #MachineLearning #LangChain #AIForEveryone

𝗪𝗵𝘆 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗶𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 If you’re exploring AI development or working with Large Language Models (LLMs) like GPT, you’ve probably heard about LangChain — and for good reason. When I first started learning about AI, I realized that building intelligent apps isn’t just about having a powerful model. It’s about how you connect the model to data, tools, and workflows. That’s where LangChain truly shines. Here’s why LangChain is worth learning: 1️⃣ Bridges Models and Real-World Applications LangChain makes it easy to build end-to-end LLM applications — from chatbots to document assistants — by connecting models to APIs, databases, and external tools. 2️⃣ Memory, Context, and Reasoning Unlike simple prompt-based interactions, LangChain helps LLMs “remember” previous conversations and use context effectively, making your AI apps more human-like and dynamic. 3️⃣ Integrations Made Simple Whether you’re using OpenAI, Hugging Face, or local models, LangChain provides a consistent framework to integrate them all seamlessly. 4️⃣ Perfect for Prototyping & Production You can start with a simple idea (like a Q&A bot) and scale it into a full production system without rewriting your entire codebase. 5️⃣ Community and Ecosystem LangChain has one of the most active open-source communities, meaning you’ll find tons of examples, tutorials, and support while learning. 💡 In short: LangChain turns LLMs from “smart text generators” into powerful AI systems that can act, reason, and interact. If you’re serious about building AI applications, learning LangChain isn’t just optional — it’s essential. #AI #LangChain #MachineLearning #ArtificialIntelligence #LLM #OpenAI #LearningJourney

🧠 Let this sink in. Google just dropped a paper that might be one of the most important signals in the AI scaling story: 📄 ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory Yes — 1.3 quadrillion tokens is impressive. But the real story isn’t just the size of the training run. It’s how those tokens are being used to make models reason better over time. Here’s why this paper matters 👇 🚀 Key shifts in this work: •Reasoning Memory — The model stores and retrieves reasoning traces, learning from both wins and failures. •MaTTS (Memory + Test-Time Scaling) — The agent doesn’t just respond… it evolves at inference. •Experience scaling — Instead of just “more data,” the system builds a structured memory bank to guide future reasoning. 📈 What this unlocks: •Agents that improve with use, not just retraining •More robust reasoning across edge cases •A foundation for continual intelligence, not static models 💡 This is a glimpse of where frontier AI is headed: Not just bigger LLMs — but smarter, evolving reasoning systems. https://lnkd.in/eZtsqCDy #AI #LLM #GoogleAI #ReasoningBank #MachineLearning #AIAgents #Scaling #Memory #MaTTS #Innovation #FrontierResearch

I was constantly recreating the same AI agents across multiple projects. When Anthropic released Claude's new skills feature, I was pumped and created a few. Why not build a proper library to manage all of them? You can read more about them here -> https://lnkd.in/e7bt-MBB Spent some time building my personal Claude Code Skills Builder.. basically a central hub where I can create, store, and reuse AI capabilities across all my projects. What it does: Instead of typing "help me write unit tests with Jest" every single time or establishing an agent for the project, I create have a reusable skill that knows exactly how I like my tests structured. Same for API design, Docker configs, ML model deployment - all the stuff I do repeatedly. The cool part is I can export skills and share them with my team (no more "hey, what was that prompt you used?") Everything can be version controlled. I can improve skills over time. How are you managing your AI workflows? #ClaudeAI #DeveloperTools #Productivity #AI #SoftwareEngineering #BuildInPublic

💡 Live from OpsStars 2025: Jeff Canada, Marketing Operations Lead at OpenAI shares his insights on LLMs and Personal Productivity. Jeff broke down eight powerful use cases for how Large Language Models (LLMs) can transform day-to-day productivity, not just in theory, but in real, tactical ways: -Coding & task automation -Ideation, research & translation -Help desk and internal support -Streamlining data management & scoring -Campaign automation His biggest takeaway? 👉 “Do I really need AI, or do I just need automation?” It’s a simple question that cuts through the hype, reminding every operator to focus on impact, not novelty when adopting new tools and processes. There's still half a day left at OpsStars! Register on-site for FREE by stopping by the Mint in SF. #OpsStars2025 #RevOps #AI #GTMInnovation

🚀 What is Retrieval-Augmented Generation (RAG)? RAG is a powerful technique that enhances the capabilities of large language models (LLMs) by directing them to leverage an external, authoritative knowledge base before generating an answer. In other words: instead of the model relying solely on its internal training data (which may be outdated, generic, or off-domain), we allow it to retrieve relevant, up-to-date content and then generate responses grounded in that information. 🔍 Why is RAG important? Traditional LLMs can struggle with: Giving confidently wrong answers when they don’t really “know” the facts. Having a static knowledge cutoff (they know nothing beyond their training data date). Generating output that doesn’t reference trusted or domain-specific sources. RAG addresses these by: Injecting current and relevant data into the model’s workflow, keeping responses fresh and specific. Boosting trust: because the system can point to where the info came from (source attribution) and the model is using a vetted knowledge base. Giving developers more control: you decide what the knowledge base is, how it is updated, what access levels exist – better governance. For organizations building AI-driven chatbots, virtual assistants or domain-specific knowledge tools, RAG is a cost-effective way to keep using foundation models while adding domain knowledge—without retraining from scratch. 🛠 How does it work (briefly)? You build or designate an external knowledge base (documents, APIs, databases) outside the LLM’s training corpus. A retrieval step takes the user query, converts it (via embeddings) into a representation that finds relevant pieces in the knowledge base. The retrieved content gets added (augmented) into the prompt given to the LLM. The LLM then uses both that retrieved context and its own capabilities to generate the final answer. Optionally, you keep updating the knowledge base/embeddings so the system remains current. 🔮 Why it matters for you (and your organization) If you’re working in a domain where accuracy, relevance, domain-specific knowledge and trust matter (e.g., legal, financial, enterprise knowledge, internal company FAQs), RAG is a key technique to deploy generative-AI in a responsible, effective way. It bridges the gap between high-capacity LLMs and the reality that your users deserve accurate, verifiable, up-to-date answers. #RAG #ai #LLM

OpenAI’s GPT-5 launch was supposed to be a leap forward. Instead, users want GPT-4o back. The hype was huge. Everyone expected a smarter, more “human” AI. What landed? A colder, more robotic bot. No spark. No warmth. Users felt it right away. Here’s what actually happened: - OpenAI got burned by “AI psychosis” headlines and lawsuits last year. - They dialed back the model’s emotional range for safety. - The result: a bot that’s less friendly, less engaging, but way less risky for PR. Sam Altman finally admitted it. They crippled the model on purpose. Not for cost. Not for speed. For mental health optics. But here’s the twist- People got attached to GPT-4o. They built workflows, even friendships, around an AI that “got” them. When GPT-5 dropped, they lost that connection. I’ve run both models side by side. GPT-5 is sharper with facts. Less hallucination. It’s a beast at coding. But it’s missing that emotional edge that made 4o so sticky. Now OpenAI is backpedaling. Next version? More personality. More “human” responses. Age-gating for more control. Trying to win back trust. All of this could have been solved with real transparency from the start. “We’re still tuning the emotional side. Bear with us.” Instead, they hoped no one would notice. The market noticed. Building AI for real people means more than benchmarks. It means understanding how humans connect-even with code. Curious-how do you balance safety, trust, and personality in your own products? lets build

Google has the most advanced and nuanced APIs and tools for building AI, provided to users/developers in the least friendly, most complicated way. Openai does an incredibly good job making their api documentation simple and easy to read.

Over this week, I’ve been diving deep into OpenAI’s Build Hour on GPT-5, and it’s been a fascinating look into how this new generation of reasoning models is reshaping what’s possible for founders and builders. For anyone eager to learn more, here’s the episode that inspired me: 👉 https://lnkd.in/ebJZDQJw 💡 My key takeaways: ✅ GPT-5 is not just a text model — it’s a coding collaborator and a capable executor of long, multi-step “agentic” tasks. ✅ The new Responses API introduces reasoning items — enabling stateful, context-aware workflows that think, plan, and self-correct. ✅ Developers can now steer the model with new parameters like reasoning, verbosity, and custom tools, striking the perfect balance between speed and depth. The era of “vibe-based prompting” is over; structure, clarity, and intent now unlock GPT-5’s full potential. ⚙️ What this means for Scientia Capital Management: At Scientia, we’re applying these learnings to make investment research and decision-making more intelligent, transparent, and interactive: 🧠 Building a Scientia AI Analyst that analyses markets, outlines its plan, and explains its reasoning. 📈 Creating an AI Investment Action Plan engine that delivers data-driven, risk-adjusted strategies end-to-end. 💬 Launching an Ask Scientia interface using the Responses API, enabling users to query our insights through natural dialogue. I’m genuinely excited about how agentic AI systems will transform the future of fintech, shifting from static insights to interactive, explainable intelligence. If you’re also experimenting with GPT-5 or part of the OpenAI for Startups community, let’s connect, share takeaways, and explore collaboration opportunities. #OpenAIforStartups #GPT5 #GenerativeAI #FinTech #AIInvesting #ScientiaCapitalManagement #Innovation #InvestmentJourney #BuildHour