Everything about llm
12 connected nodes across dictionary, workflows, comparisons, prompts, tool stacks and use cases.
LLM (Large Language Model)
A model trained on huge text corpora that predicts the next token to produce human-like language.
Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
Local LLM
A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
Reasoning Model
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
Token Budgeting
Token budgeting is the strategic allocation and management of token usage within large language model (LLM) operations to control costs and optimize performance.
AI Search Ranking Factors
AI search ranking factors are the criteria and signals used by generative AI models to determine the relevance, quality, and trustworthiness of information when formulating responses or generating content.
Run an LLM Locally
Set up a private, offline language model on your own computer in under an hour.
Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.