Learn how retrieval models, embedding vectors and retrieval-augmented generation are combined in modern AI systems. This article covers semantic search, the role of embeddings in supporting generative models, and the practical trade-offs of embedding-based retrieval.
An overview of how generative models produce text, images and code in modern AI systems. We look at how next-token prediction works in practice, why generative models can sound fluent without truly understanding, and how generation is typically combined with retrieval.
An overview of how generative models produce text, images and code in modern AI systems. It explains how next-token prediction works in practice, why generative models can sound fluent without truly understanding, and how generation is typically combined with retrieval and tools to produce reliable
Learn how retrieval models, embedding vectors and retrieval-augmented generation are combined in modern AI systems. This articles covers semantic search, the role of embeddings in supporting generative models, and the practical trade-offs of embedding-based retrieval.
AI tools are now part of how many students learn. Used well, they can support understanding and independent practice. The real challenge for schools is how to assess learning fairly and meaningfully when these tools are part of the process.
As ChatGPT becomes more widely used, many people treat it like a search engine — typing in questions and expecting reliable answers, much like they would with Google. But while the user experience may feel similar, the underlying technology is fundamentally different.
The original Paperclip Maximiser thought experiment reimagined as a fictional story. We explore what happens when an artificial intelligence system is given a simple goal — and follows it with perfect logic, but no understanding of human consequences.
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