Did You Know
Local LLM vs. Cloud LLM
A local LLM runs privately on your own hardware with no usage fees, while a cloud LLM runs on a provider's servers and trades privacy and cost for more capable models.
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Did You Know
A local LLM runs privately on your own hardware with no usage fees, while a cloud LLM runs on a provider's servers and trades privacy and cost for more capable models.
Definition
A multimodal AI understands more than just text — it can take in and respond with images, audio, or video too.
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An open-weights AI model is one you can download and run yourself, though unlike true open source, the training data and recipe usually stay private.
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Hallucination is when an AI states something false with full confidence, instead of admitting it doesn't know the answer.
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Chunking splits a long document into smaller pieces so an AI system can search and work with just the relevant part instead of the whole thing at once.
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Re-ranking is a second, more careful pass that reorders an initial batch of search results so the best matches end up at the top.
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A vector database stores embeddings so AI applications can retrieve information by meaning and similarity instead of exact keywords.
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Semantic search matches queries by meaning, helping it find relevant content even when the exact keywords are different.
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An embedding is a list of numbers that captures a piece of text's meaning, letting an AI system compare ideas by similarity instead of exact word matches.
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Tool calling lets an LLM request a specific action instead of just generating text, then wait for the result before continuing — it can't run anything itself.
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An LLM is an AI model trained on huge amounts of text to predict the next word — a simple task that, at scale, produces writing, summarizing, and conversation.
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RAG (Retrieval-Augmented Generation) feeds an LLM relevant external data at query time, so it can answer questions about information it was never trained on.