Topics

Fundamentals

• EmbeddingsAn embedding is a vector (a list of numbers) that represents the meaning of a piece of text, image, or other data. Items with similar meaning end up close together in vector space, so "find…
• Large language modelsA large language model (LLM) is a neural network trained to predict the next token in a sequence of text. Trained on a huge corpus and then tuned to follow instructions, that one skill turns into…
• Reasoning models and extended thinkingReasoning models generate internal reasoning ("thinking") before their final answer. Spending more compute at inference time this way improves results on maths, coding, planning, and multi-step…
• Retrieval-augmented generation (RAG)RAG answers a question by first retrieving relevant information from an external source and then giving it to the model as context. It keeps answers current, grounded, and citable without…
• Tokens and context windowsModels read and write tokens, not characters or words. A tokenizer splits text into sub-word pieces from a fixed vocabulary. Everything about using an LLM is measured in tokens: price, speed, rate…
• Training and fine-tuningLLMs are built in stages. Pre-training gives broad knowledge and language skill; post-training turns the result into a helpful, safe assistant. Fine-tuning adapts a trained model further for a…
• TransformersThe transformer is the neural network architecture behind nearly every modern LLM. It was introduced in 2017 for translation and replaced recurrent networks because attention can process a whole…