llm

How LLMs Work – Deep Technical Overview

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Large Language Models are now embedded in search, content workflows, analytics tools and marketing platforms. But before asking how to optimize for them, influence them or measure visibility inside them, there is a more basic question we need to answer: What is the system actually doing? That question sounds obvious, but many of the expressions … Read more

LLM Pre-Training Explained: Tokens, Data & Model Weights

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The foundation of a Large Language Model begins with pre-training: the computationally intensive stage in which the model learns statistical structure from a very large collection of examples. The important word here is learns, because it is easy to give it a human meaning it does not have. During pre-training, the model is not reading … Read more

Inside LLMs: Neural Networks & Attention

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What happens between a sentence entering a language model and the model producing an answer? At the transformer level, three ideas do much of the explanatory work: numerical representations, attention and sequence structure. Together they help explain how information in the available context can interact while a model computes its next output. For marketers, this … Read more

Inside LLMs: RLHF, RLAIF & the Evolution of Model Alignment

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Pre-training gives a language model a broad ability to continue text, represent patterns and perform many tasks. But that alone does not make it behave like the assistant a user expects. A user usually wants something more specific: follow the instruction, respect the requested format, preserve important conditions, avoid unsupported claims and communicate uncertainty when … Read more

Inside LLMs: why LLMs don’t really “know” things

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Large language models create an uncomfortable combination: they can answer difficult questions remarkably well and still make elementary factual mistakes. The easy explanation is to say that LLMs “do not really know anything”. But that statement is too blunt to be useful. If a system correctly compares two products, follows a technical specification or solves … Read more

Inside LLMs: Understanding Transformer Architecture – A Guide for Marketers

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The previous articles introduced pre-training and embeddings, positional information and attention. Now we can put those pieces together. What actually happens between token representations entering a language model and the model producing scores for the next token? The answer matters because transformer architecture attracts unusually convenient marketing myths. Attention becomes “make these words bold”. Learned … Read more