At #FICC2025, Aljoscha Burchardt unpacked how Large Language Models (LLMs) actually work — and why their limitations matter just as much as their capabilities. From training data and reasoning gaps to bias, hallucinations, and accountability, the session offered a clear-eyed look at the realities behind today’s AI systems. As LLMs increasingly influence journalism, research, and public discourse, understanding both their power and their pitfalls is no longer optional — it’s essential. Watch the
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LLMs can make sense of retrieved context because of how transformers work. In one of the lessons from the Retrieval Augmented Generation (RAG) course, we unpack how LLMs process augmented prompts using token embeddings, positional vectors, and multi-head attention. Understanding these internals helps you design more reliable and efficient RAG systems. Watch the breakdown and keep learning how to build production-ready RAG systems in this course, taught by Zain Hasan: https://hubs.la/Q03zPJyR0 |
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Measuring the impact of Large Language Models (LLMs) goes beyond just tracking usage numbers. At Defyn Digital, we believe true visibility means understanding how LLMs drive real business outcomes: • Are they improving user engagement? • Do they boost conversion rates or customer satisfaction? • How do they work with your existing digital ecosystem to create value? For example, one client saw a 30% increase in customer interactions after adding an LLM-powered chatbot tailored to their brand voic
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