ki-konzepte LLM-Grundlagen
Embeddings Simply Explained
An embedding is a vector of numbers that places meaning in space. Why similar content sits close together and what embeddings are used for.
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LLM-Grundlagen
Basis-Begriffe rund um Large Language Models.
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- ki-konzepte LLM-Grundlagen
LLM Hallucinations — Causes and Remedies
Why LLMs confidently invent falsehoods, what types of hallucinations exist, and which remedies actually help — RAG, source enforcement, verification.
Redaktion - ki-konzepte LLM-Grundlagen
Temperature, Top-p and Sampling in LLMs
How an LLM picks the next token: temperature sharpens or flattens the probabilities, top-p and top-k limit the choice. Which setting for what.
Redaktion - ki-konzepte LLM-Grundlagen
The Context Window of LLMs — What Fits and What Doesn't
What the context window is, how big modern windows are, the lost-in-the-middle phenomenon, cost and latency, and the distinction from RAG and long-term memory.
Redaktion - ki-konzepte LLM-Grundlagen
Tokens and Tokenization in LLMs Explained
What a token is, how tokenizers split text, and why tokens drive cost, context window, and speed — with rules of thumb for estimating token counts.
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