Terminology

Glossary of AI and Honcho Specific Terms

AI Development Basics

Essential terms for developers new to building AI applications.

​LLM (Large Language Model)

The AI model that generates text responses, like GPT-4, Claude, or Llama. Think of it as the “brain” that powers your chatbot or AI assistant.

​Prompt

The text you send to an AI model to get a response. This includes user messages, system instructions, and any context you provide.

​Token

How AI models count and limit text. Roughly 1 token = 0.75 words. Models have token limits (like 4,000 or 128,000 tokens) that determine how much text they can process at once.

​Context Window

The maximum amount of text an AI model can “remember” in one conversation. Once you exceed this limit, the model starts “forgetting” earlier parts of the conversation.

​Embedding

Converting text into numerical vectors that computers can understand and compare. Enables “smart search” that finds similar content based on meaning, not just keywords.

​Semantic Search

Search based on meaning rather than exact keyword matching, often using embeddings.

​Agent

An AI system that can take actions and make decisions, not just generate text responses. Agents can use tools, call APIs, and interact with external systems.

​Honcho Terms

​Global Representation

Derived context of a specific peer, synthesizing insights from interactions across all sessions, including arbitrary data ingested by this specific peer. With arbitrary data, a global representation can be made independent of sessions.

​Local Representation

One peer’s persistent context of another based on observed interactions/messages.

​Cognitive Science Terms

Cognitive science terms that are used throughout the inspiration and implementation of Honcho

​Theory of Mind

The ability of a computer to understand, remember, and interact with its own mind, enabling it to form representations of the world and make decisions based on its own knowledge and behavior.

​Social Cognition

The mental processes by which we perceive, interpret, and respond to information about others and social situations. It includes the encoding, storage, retrieval, and application of social knowledge.

​Cognitive Architecture

In CogSci, frameworks describing fixed structures & mechanisms underlying human cognition. Such frameworks aim to explain how various components of the mind—perception, memory, reasoning, learning, etc—combine to produce intelligent behavior across diverse environments. In AI, it’s a computational implementation of these theories—a designed framework to replicate human cognitive functions.

​Predictive Coding

A theory in CogSci proposing the brain is an active prediction machine, continually generating & updating internal world models to anticipate sensory input, rather than passively receiving it—closely linked to Bayesian brain hypotheses, which hold that the brain interprets the world probabilistically, weighing prior knowledge against new evidence to minimize uncertainty.