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Discover our new blog post about LLMs in Action: What Works, What Doesn’t Large Language Models (LLMs) are powerful, but not every use case delivers real value. Our new blog series “Evaluating LLM Applications” (Part 1 of 2) written by our expert Niklas Ullmann reveals practical tips, pitfalls to avoid, and strategies to make LLMs work for your business. In the next article, we’ll provide a technical deep dive into RAGAS implementation with a practical case study — showing how this framework turns LLM evaluation from an art into a science, with concrete examples for technical teams. 🔗 Read Part 1 now: https://okt.to/MaZIjF #AI #LLM #LargeLanguageModels #MachineLearning #DataDriven

Discover our new blog post about #LLMs in Action: What Works, What Doesn’t Large Language Models (LLMs) are powerful, but not every use case delivers real value. Our new blog series “Evaluating LLM Applications” (Part 1 of 2) written by our expert Niklas Ullmann reveals practical tips, pitfalls to avoid, and strategies to make LLMs work for your business. In the next article, we’ll provide a technical deep dive into RAGAS implementation with a practical case study — showing how this framework turns LLM evaluation from an art into a science, with concrete examples for technical teams. 🔗 Read Part 1 now: https://okt.to/PvZWbG #AI #LLM #LargeLanguageModels #MachineLearning #DataDriven #synvert

AI Agents from First Principles Large language models (LLMs) are improving quickly. These models can understand and generate text with a high level of skill. As they become more powerful, we can use them to build systems that handle harder tasks, connect with the outside world, and work over longer periods of time. These systems are called AI agents. Full blog post: https://lnkd.in/gy_HkX2h #GenAI  #AI  #LLM  #PromptEngineering  #ContextEngineering #LangGraph

🧠Maybe think small first? Small Language Models (SLMs) are gaining traction because they’re lightweight, faster, and easier to fine-tune for specific use cases. Not every problem needs a massive LLM; sometimes a smaller, task-optimized model does the job just as well. Still, LLMs exist for a reason! They handle reasoning, context, and generalization at a scale SLMs can’t (yet). What’s exciting is the possibility that the future won’t be LLMs vs SLMs, but rather LLMs and SLMs in tandem, each covering what they do best. #AI #SmallLanguageModels #MachineLearning #LargeLanguageModels

Taking my first step into the world of Transformers and Attention Mechanisms — the backbone of modern Large Language Models (LLMs). For anyone starting out, I highly recommend this free course by Analytics Vidhya : https://lnkd.in/eNRuJfJs #AI #LLMs #Transformers #DeepLearning #MachineLearning

Retrieval augmented generation (RAG) takes AI to the next level. Linking large language models (LLMs) with external knowledge helps generate smarter, more relevant, and higher-quality answers. However, not all databases are built with RAG in mind...🤔 Check out our latest infographic and discover six key factors to help you make a smart, scalable RAG database choice for your business.⬇️ https://lnkd.in/d_bv3S9G #RAG #ai #GenAI #distributedsql #yugabytedb #Vector

NEW TECH LAUNCH: Motive is enhancing its offerings with a natural language AI tool. Learn more about the new tech in the article below. ⬇️ https://lnkd.in/g_HPwnGX #BuiltInSF #SanFranciscoTech

A tiny model with only 7 million parameters outperforms Deepseek R1, o3-mini, and Gemini 2.5 on many benchmarks In a landscape where many Large Language Models (LLMs) are becoming increasingly "enormous," the Tiny Recursive Model (TRM) draws a completely different approach: it uses a small network and recursion to perform deep reasoning - achieving high efficiency with a modest parameter count (7 million parameters). On some difficult tasks, this model even performs better than LLMs that are tens of thousands of times larger. Paper link: https://lnkd.in/gURhtsEJ #AI #LLM #MachineLearning #TinyModels #Research #DeepLearning

Turn your data into real-time answers! With natural language queries, unified insights for every team, and instant actions, it’s time to step into the future of decision-making. Dive into our latest blog to see how AI is reshaping enterprise decision-making. Read the full blog: https://lnkd.in/gKZtRyGt #DataDriven #AI #DecisionMaking #NaturalLanguageProcessing #askme360 #BusinessIntelligence #RealTimeInsights #EnterpriseAI #FutureOfWork #HeuristicsInformatics

Evaluating large language models (LLMs) often relies on subjective human evaluation. Experts manually assess outputs, tweaking prompts based on perceived accuracy. However, objective benchmarks like FinanceBench and RAG-Instruct offer labeled datasets for cosine similarity or BLEU score analysis, enabling string and chunk matching. The challenge arises with unstructured real-world data, making it difficult to gauge answer correctness without human oversight. The key is to move beyond LLM evaluation and incorporate human review for reliable assessment. #LLMs #FinanceBench #RAGInstruct #AI #DataAnalysis