Tag: llm-concepts

All the articles with the tag "llm-concepts".

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  llm-concepts

  29 Apr, 2026 · 7 min read

  Hallucinations and Jailbreaks: The Two Ways LLMs Fail

  LLMs produce confident wrong answers and can be tricked into ignoring safety rules. What is actually happening and why both failures are hard to fix.

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  llm-concepts

  28 Apr, 2026 · 8 min read

  The 2026 Model Lineup: Who Ships What

  A field guide to the 2026 frontier and open-weight model field, and a practical way to think about which model to actually pick.

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  llm-concepts

  27 Apr, 2026 · 7 min read

  Multimodality: Teaching Models to See and Hear

  A multimodal model is not many models in a trench coat. It is one transformer trained to treat pixels, audio, and text as the same kind of thing.

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  llm-concepts

  27 Apr, 2026 · 7 min read

  Reasoning Models: Chain-of-Thought and Test-Time Compute

  Reasoning models do not have a new architecture. They have a new training recipe and permission to think for longer before answering.

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  llm-concepts

  23 Apr, 2026 · 7 min read

  Modern Alignment: RLHF, DPO, and Constitutional AI

  A base model just predicts tokens. Alignment turns it into an assistant that follows instructions and refuses harmful ones.

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  llm-concepts

  23 Apr, 2026 · 8 min read

  Mixture of Experts: Why 671B Does Not Equal 671B

  A 671B Mixture of Experts model can be faster and cheaper to run than a dense 70B. The headline parameter count stopped meaning what it used to mean.

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  llm-concepts

  23 Apr, 2026 · 6 min read

  Parameter Counts and Scaling Laws: What 70B Actually Means

  What does 70B actually mean? It tells you about memory requirements, inference speed, and training costs, but almost nothing about model quality on its own.

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  ai

  21 Apr, 2026 · 8 min read

  Context Windows: Why Your AI Has a Working Memory Limit

  Context windows are not memory. They are working memory. Here is what the model can see right now, why extending that limit is hard, and what it costs to try.

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  ai

  20 Apr, 2026 · 9 min read

  Positional Encoding and Sampling: How the Transformer Finds Position and Picks Its Next Word

  Attention cannot tell 'the dog bit the man' from 'the man bit the dog.' Positional encoding fixes that. Then sampling decides what word the model actually says.