SensorLM: Learning the language of wearable sensors
Generative AI
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How to optimize LLM performance and output quality: A practical guide
Have you ever asked generative AI the same question twice – only to get two very different answers? That inconsistency can be frustrating, especially when you’re building systems meant to serve real users in high-stakes industries like finance, healthcare, or law. It’s a reminder that while foundation models are incredibly powerful, they’re far from perfect….
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Turning structured data into ROI with genAI
At GigaSpaces, we’ve been in the data management game for over twenty years. We specialize in mission-critical, real-time software solutions, and over the past two decades, we’ve seen just how essential structured data is, whether it resides in a traditional database, an Excel sheet, or a humble CSV file. Every company, regardless of its size…
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Building enterprise-scale RAG applications with Amazon S3 Vectors and DeepSeek R1 on Amazon SageMaker AI
Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI,…
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The Art and Science of Fine-Tuning LLMs for Domain-Specific Excellence
Key advancements include in-context learning, which enables coherent text generation from prompts, and reinforcement learning from human feedback (RLHF), which fine-tunes models based on human responses. Techniques like prompt engineering have also enhanced LLM performance in tasks such as question answering and conversational interactions, marking a significant leap in natural language processing. Pre-trained language models…
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Building & securing AI agents: A tech leader crash course
The AI revolution is racing beyond chatbots to autonomous agents that act, decide, and interface with internal systems. Unlike traditional software, AI agents can be manipulated through language, making them vulnerable to attacks like prompt injection and they also introduce new security risks like excessive agency. Join us for an exclusive deep dive with Sourabh…
