Isha Rani

🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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🎯 Hard Truths About Enterprise adoption of Generative AI Just wrapped an incredible discussion on "From Pilot to Production" with enterprise AI leaders from Berenberg, Bayer, and Continental. At Generative AI Summit, hosted at Ritz Carlton hotel at Berlin on 11th Sep 2025. Here are the key insights from the panel discussion: #1: Pilots prove ideas, production creates value Creating a Gen AI pilot is exciting and shows incredible potential. But the real value only emerges when that output seamlessly integrates with your existing processes. The biggest hurdles? User adoption, trustworthiness, governance, and the unpredictable nature of Gen AI models during production deployment. My biggest takeaway: Start thinking about user concerns, governance frameworks, and hallucination challenges from day one of your pilot. Don't wait until production to tackle these critical issues. Grateful to my fellow panelists who brought game-changing perspectives: Soenke von Schassen shared powerful insights on financial services AI governance, Youssef Essa opened our eyes to trust and compliance challenges, and Aditi Bhalla shared interesting insights from the Automotive Sector. Thanks to Albert Chitiyo for the invitation to join the Generative AI summit. It was worth the time, full of insights. Great to meet some of my old colleagues, Prashanth Halady, Andreea Salinca-Mihai, PhD. For engineering leaders scaling AI: Focus on the human layer first. You can solve technical problems with the right investment. Cultural alignment? That requires intentional strategy and genuine leadership. I'm curious about your journey: Where do your Generative AI initiatives typically hit roadblocks—technical hurdles or adoption challenges? What's been your biggest lesson learned? #EnterpriseAI #AITransformation #TechLeadership #GenerativeAI #ProductionAI #PanelInsights

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Ready to unlock the future of AI and ML in 2025? → General-Purpose AI / ML Frameworks   • These frameworks are the backbone of AI development.  • Expect enhancements in TensorFlow and PyTorch, making them more intuitive and powerful.   • The integration of pre-built models will allow faster deployment and experimentation. → Vector Databases   • As the demand for real-time data processing increases, vector databases will rise to the occasion.   • They excel in managing unstructured data for AI models - think performance and scalability. → Generative AI / Text to Image   • Imagine turning words into stunning, lifelike images with just a sentence.  • Tools like DALL-E and Midjourney are leading this charge.   • The boundary between creativity and technology is disappearing, and content creation will never be the same. → No-Code / Low-Code AI Platforms   • Empowering non-technical users is the goal.  • Expect platforms that eliminate barriers and democratize AI.   • Imagine building robust solutions with drag-and-drop simplicity! → AI Agent & Multi-Agent Systems   • The future belongs to systems that can learn, adapt, and communicate seamlessly.  • Inter-agent collaboration will drive efficiencies in various sectors.   • We’re entering an era where machines won’t just assist us; they’ll strategize with us. → LLM Application Frameworks   • Large Language Models are set to become more accessible.  • Developers will create tailored applications without starting from scratch.   • Chatbots and virtual assistants will be smarter, providing personalized experiences. Follow Habib Shaikh for more insights

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𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝟰.𝟬 is IBM's new flagship family of open-source, enterprise-ready AI foundation models, targeting efficient, scalable deployment for a range of business needs. 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝟰.𝟬? Granite is IBM’s suite of large language models robustly designed for mission-critical enterprise tasks like Retrieval Augmented Generation (RAG), customer support, and agent-centric workflows. Granite 4.0 officially launches as open-weight models spanning 3B to 32B parameters, making them suitable across a spectrum of hardware and deployment scenarios. 𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝟰.𝟬 𝗠𝗼𝗱𝗲𝗹 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗮𝗺𝗯𝗮/𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 architecture allows significantly lower memory needs (over 70% reduction for long contexts/many sessions)—enables cost-effective operation even on mid-tier GPUs. 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗜𝗦𝗢 𝟰𝟮𝟬𝟬𝟭 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱, underlining IBM’s focus on security, accountability, and transparency standards in AI model management. All model weights are cryptographically signed to verify authenticity and provenance. Collection covers several styles: 𝗚𝗿𝗮𝗻𝗶𝘁𝗲-𝟰.𝟬-𝗛-𝗦𝗺𝗮𝗹𝗹: 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗶𝘅𝘁𝘂𝗿𝗲-𝗼𝗳-𝗘𝘅𝗽𝗲𝗿𝘁𝘀 (𝗠𝗼𝗘), 𝟯𝟮𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀, 𝟵𝗕 𝗮𝗰𝘁𝗶𝘃𝗲. 𝗚𝗿𝗮𝗻𝗶𝘁𝗲-𝟰.𝟬-𝗛-𝗧𝗶𝗻𝘆: 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗘, 𝟳𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀, 𝟭𝗕 𝗮𝗰𝘁𝗶𝘃𝗲. 𝗚𝗿𝗮𝗻𝗶𝘁𝗲-𝟰.𝟬-𝗛-𝗠𝗶𝗰𝗿𝗼: 𝗗𝗲𝗻𝘀𝗲 𝗵𝘆𝗯𝗿𝗶𝗱, 𝟯𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀. 𝗚𝗿𝗮𝗻𝗶𝘁𝗲-𝟰.𝟬-𝗠𝗶𝗰𝗿𝗼: 𝗣𝘂𝗿𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿, 𝟯𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀—𝗺𝗲𝗮𝗻𝘁 𝗳𝗼𝗿 𝗹𝗲𝗴𝗮𝗰𝘆 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝘂𝗻𝗮𝗯𝗹𝗲 𝘁𝗼 𝗿𝘂𝗻 𝗵𝘆𝗯𝗿𝗶𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. The models are available in both base and instruction-tuned variants, with planned future releases for dedicated reasoning models. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 >> Granite 4.0 models stand out for high inference efficiency and competitive benchmark results, making them suitable for large enterprise use and rapid deployment. >> Hybrid architecture blends attention (transformer) with Mamba-2 layers for scalable processing of long documents or multi-agent workloads, ideal for concurrent, real-time enterprise scenarios. >> Instruction-tuned models excel at dialogue, multilingual tasks, summarization, code, function-calling, RAG, and more. >> Trained on a large and diverse corpus (22T tokens), covering language, code, math, reasoning, safety, tool usage, and cybersecurity use cases. 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 Granite 4.0 models are released under Apache 2.0, accessible on IBM watsonx.ai, Hugging Face, Docker Hub, NVIDIA NIM, Kaggle, Ollama, Replicate, Dell AI Hub, and soon AWS & Azure. Full announcement (IBM): https://lnkd.in/dcYG_NdZ Hugging Face model collection: https://lnkd.in/dT7_6Prh GitHub repo & code: https://lnkd.in/d9atrNAi Granite 4.0 docs: https://lnkd.in/dZ75XyvM

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