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Diffusion models: a plain-language guide to denoising
Diffusion models create samples through a learned denoising process. The process is mathematical, even when the result looks like a sketch emerging from…
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Prompt injection: treating outside content as untrusted
Outside content can contain instructions aimed at an AI assistant. Reading that content does not give it authority.
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Context windows: why more space is not perfect memory
A large context window gives a model more material to consider. It does not provide perfect recall or automatic prioritisation.
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AI video: plan a shot before generating a clip
AI video becomes easier to direct when each request describes a shot with a clear subject, motion and endpoint.
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AI privacy: map the data before choosing a workflow
Privacy becomes easier to assess when you trace the actual movement of information.
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Embeddings: turning meaning into searchable numbers
Embeddings represent items as numerical vectors so software can compare patterns of similarity.
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Speech recognition: turning audio into workable text
Speech recognition converts audio into text. A readable transcript still needs checks against the recording.
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AI bias: look at who is missing from your evaluation
An average score can hide a system that works poorly for part of its audience.
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Vector search: finding ideas beyond exact words
Vector search helps find related ideas. Its value depends on the documents, filters and ranking around it.
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Voice synthesis: designing a clear and responsible voice
A useful synthetic voice is clear, appropriate to its setting and created with the necessary permission.
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Accessibility: design AI output that more people can use
Accessible AI output depends on both the information and the way people interact with it.
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RAG: giving an assistant the right source material
Retrieval-augmented generation gives a model selected source material at answer time. The retrieval step deserves as much attention as the wording.
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