Skip to content
AI News HubLIVE
More
Summary only

How AI Works Under the Hood – LLMs Explained with Code

A deep dive into the inference pipeline of Large Language Models, from tokenization and embeddings to self-attention and next-token prediction, illustrated with JavaScript code examples. Covers training (pre-training and alignment), scaling techniques (MoE, multimodal, quantization), and high-level applications like function calling, chain of thought, RAG, and agents.

SourceHacker News AIAuthor: thunderbong
How AI Works Under the Hood – LLMs Explained with Code
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions

Summary only

Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

Article intelligence

EngineersAdvanced

Key points

  • LLMs tokenize text into subword units using BPE, then map them to embeddings and add positional encodings.
  • The Transformer architecture uses self-attention to let each token attend to previous tokens, building context through stacked layers.
  • Training involves pre-training on vast text corpora and post-training (RLHF/DPO/CAI) to align with human preferences.
  • Scaling techniques include Mixture of Experts, multimodal encoders, and quantization; higher-level features like tool use and RAG build on the core pipeline.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Models