I don't understand AI. How does it work?

2026-04-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, long

Summary

Large Language Models (LLMs) formulate answers by predicting the next most probable word or token based on statistical relationships learned from vast training data, rather than performing real-time web searches. This process, often described as advanced autocomplete, involves converting words into high-dimensional numerical vectors (embeddings) that represent semantic relationships. For example, "King" - "Man" + "Woman" can semantically equate to "Queen" within this vector space. LLMs learn the global structure of language and concepts, understanding how ideas inter-relate. The prevalence of stylistic elements like em dashes in LLM outputs is attributed to post-training data patterns, where human raters favor such text, and also to models being recursively trained on the outputs of their predecessors, perpetuating these stylistic choices unless data is cleaned.

Key takeaway

For AI Product Managers evaluating LLM outputs, recognize that stylistic elements like em dashes are often artifacts of training data and reinforcement learning, not inherent "intelligence." Your teams should consider data cleaning or fine-tuning strategies to mitigate undesirable stylistic patterns and ensure outputs align with brand voice and clarity standards, rather than perpetuating learned biases from previous model generations.

Key insights

LLMs predict text by mapping words to semantic vectors and learning statistical relationships from vast training data.

Principles

• Language has a deep, learnable semantic structure.
• LLMs learn relationships between ideas, not just words.

Method

LLMs convert input into numerical vectors, predict the next most likely token based on learned statistical patterns, and iteratively generate responses until an "end of document" token is predicted.

In practice

• Use specific prompts to elicit more detailed LLM responses.
• Be aware of stylistic biases in LLM outputs due to training data.

Topics

• Large Language Models
• Word Embeddings
• Semantic Space
• Next-Token Prediction
• Reinforcement Learning

Best for: AI Student, General Interest, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.