Neelam Yadav @XTechOps
Follow me if you like Python, ML, RAG, AI Agents, MLOps, LLMOps & DevOps India, Gurgaon Joined September 2011-
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Raw documents are not AI-ready. They need to be ingested, cleaned, structured, chunked, embedded, indexed, and validated. AIRDops brings these steps into one repeatable workflow. Demo: youtube.com/watch?v=QLyesq… GitHub: github.com/atulX7/airdops #GenAI #MLOps
Changing chunk size, overlap, or preprocessing should not require rebuilding your pipeline every time. AIRDops lets teams quickly experiment, compare results, and identify the best data-processing approach. Demo: youtube.com/watch?v=QLyesq… GitHub: github.com/atulX7/airdops #RAG #DataEngineering
The biggest delay in enterprise AI is often not the model. It is the time spent cleaning data, testing chunking, creating embeddings, indexing content, and validating results. AIRDops helps automate that journey. Demo: youtube.com/watch?v=QLyesq… GitHub: github.com/atulX7/airdops #EnterpriseAI
30 days. 2 builders. 1 goal: make enterprise data AI-ready faster. We built AIRDops—an open-source platform for AI, ML, RAG, LLM, search, and agentic use cases. Demo: youtube.com/watch?v=QLyesq… GitHub: github.com/atulX7/airdops #OpenSource #AI
The latest 𝕏 algorithm has been published to GitHub github.com/xai-org/x-algo…
A practical GenAI lesson: Do not trust AI because it explains itself well. Trust it because you built systems around it that make it reliable. For me, that means: ➡️ retrieval ➡️ grounding ➡️ policy checks ➡️ evals ➡️ human verification where needed AI trust is a system design problem.
A practical GenAI lesson: Do not trust AI because it explains itself well. Trust it because you built systems around it that make it reliable. For me, that means: ➡️ retrieval ➡️ grounding ➡️ policy checks ➡️ evals ➡️ human verification where needed AI trust is a system design problem.
A simple way to think about hallucination: Sometimes AI does not hallucinate because it wants to make things up. It hallucinates because it thinks it knows the answer when it actually doesn’t. That’s a huge difference. The fix is not just “prompt better.” The fix is better grounding, retrieval, and validation.
𝗧𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗻𝗲𝗲𝗱𝘀 𝗺𝗼𝗿𝗲 𝗘𝘂𝗴𝗲𝗻𝗲 𝗥𝗼𝘀𝗵𝗮𝗹. The 𝗺𝗮𝗻 𝗯𝗲𝗵𝗶𝗻𝗱 𝗪𝗶𝗻𝗥𝗔𝗥. And yes, RAR literally stands for Roshal Archive. Back in 1993, he created the RAR format. Then in 1995, he released WinRAR for Windows. And then he accidentally created one of the greatest distribution hacks in software history: “𝟰𝟬-𝗱𝗮𝘆 𝗳𝗿𝗲𝗲 𝘁𝗿𝗶𝗮𝗹.” But here’s the funny part everyone remembers: The reminder showed up. People clicked OK. And kept using it anyway. For years. Sometimes decades. No drama. No aggressive lockouts. No “your files are blocked until you pay” nonsense. Just a product that solved a real problem: smaller files easier sharing cheaper storage better compression That’s why WinRAR spread everywhere. School computers. Office desktops. Internet cafes. Home PCs. In a world obsessed with growth hacks, WinRAR won with something simpler: make useful software and become impossible to uninstall from human habit. Absolute legend. #WinRAR #EugeneRoshal #Software #TechHistory #BuildInPublic
𝗚𝗼𝗼𝗴𝗹𝗲 𝗱𝗶𝗱𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵 𝗚𝗲𝗺𝗺𝗮 𝟰. They launched a 𝗟𝗢𝗖𝗔𝗟 𝗔𝗜 𝗳𝗹𝗲𝘅. 🔥 Everyone talks about benchmarks. I care about this: Can I run it on my own machine? With Gemma 4, the answer is yes. Google’s official docs explicitly support local/on-device usage, and publish memory guidance for local inference too. The standout: 26B A4B MoE 26B total params 4B active per token So this launch is not just about model size. It’s about making serious AI more practical locally. #Gemma4 #GoogleAI #MoE #OpenSourceAI #LLM #GenAI
One thing I’ve noticed with AI: Sometimes it gives the right answer but the explanation it gives for how it got there is not the real one. That’s why in production, I don’t trust “nice reasoning steps” blindly. Lesson: Good output does not always mean faithful reasoning. Always verify
Its the reality. Every non tech person has now become highly technical person in there opinion
@waelnassaf Lets connect. I am into building AI tools
RAG vs Agentic RAG : Lets simplify it RAG = User asks a question → system retrieves relevant documents → LLM answers from that context 📚 It is great for: ✅ grounded answers ✅ enterprise knowledge search ✅ reducing hallucinations ✅ fast Q&A over your data But mostly, it is still a retrieve + respond pattern. Agentic RAG = The system does not just retrieve once and answer. It can: 🔎 rewrite the query 🧭 decide which source/tool to use 📂 retrieve from multiple places 🧪 validate what it found 🔁 loop again if context is weak 📝 then generate the final answer So the shift is: RAG = fetch context and answer Agentic RAG = reason, plan, retrieve, verify, then answer #AI #RAG #AgenticAI #AgenticRAG #LLMOps #GenerativeAI #EnterpriseAI
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