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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.

Memory
How your AI application remembers information between conversations. Without memory, each conversation starts fresh with no knowledge of previous interactions.

Context
Background information your AI knows about the current situation, user, or conversation. Good context leads to more relevant and personalized responses.

Personalization
Tailoring AI responses to each specific user based on their preferences, history, and behavior patterns.

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.

Metadata
Extra information attached to your data, like tags, timestamps, or custom properties. Helps organize and filter your content.

API (Application Programming Interface)
How your application communicates with external services. APIs define what requests you can make and what responses you’ll get back.

Endpoint
A specific URL your application calls to perform an action, like creating a user, sending a message, or retrieving data.

Session
One complete conversation or interaction between a user and your AI. Sessions help organize and maintain context within individual conversations.

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.

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Honcho Core Concepts

The fundamental building blocks of the Honcho platform.

These four concepts form the foundation of Honcho’s architecture and enable all other features.

Workspace
A top-level container providing complete isolation between different applications or environments. Workspaces enable multi-tenancy and contain all peers, sessions, and data with workspace-scoped authentication.

Peer
A persistent identity within a workspace that represents a conversational participant (human users, AI agents, or other possible entities). Peers maintain persistent context and memory across all their interactions and can be configured with specific behavioral settings.

Session
An independent conversation or interaction context that can include multiple peers. Sessions exist independently, allowing persistent context as well as dynamic peer participation and removal.

Message
A single communication unit within a session, attributed to a specific peer. Messages support rich content, metadata, and automatic background processing for derived insights.

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Multi-Peer Architecture

Advanced features for complex conversational scenarios.

Multi-Peer Sessions
Sessions that support multiple participants simultaneously, including humans, AI agents, and other entities. Each peer can have individual configuration settings within the session.

Peer Configuration
Settings that control how a peer behaves within a session:

  • observe_me: Whether this peer’s actions are observed and learned from
  • observe_others: Whether this peer learns from other participants

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Context & Memory

How Honcho builds and maintains intelligent context about users and conversations.

Understanding these concepts is key to leveraging Honcho’s Theory of Mind capabilities.

The Deriver
Honcho’s core engine that uses Theory of Mind principles to extract facts, maintain context, and create representations of any given peer.

Peer Context
Persistent knowledge and preferences associated with a specific peer that spans across all their sessions. This includes personality profiles, preferences, and accumulated facts.

Session Context
Conversation-specific memory and context that is scoped to individual sessions, including summaries and session-specific insights.

Arbitrary Data
Information fed to the deriver to jump-start a representation or provide extra context to a specific peer, independent of natural conversation.

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.

Observer Peer
A peer that forms representations of other peers based on their interactions.

Target Peer
The peer being observed to inform a representation.

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Storage & Processing

How Honcho stores and processes conversational data.

Dialectic API
Natural language interface for making queries about what insights have been derived concerning global and local peer representations.

Collections
Internal storage containers that use vector embeddings (numerical representations of text) to enable semantic search and similarity matching. Collections store insights, facts, and context about peers generated by the Deriver, allowing the Dialectic API to find relevant information based on meaning rather than exact keywords.

Documents
Internal pieces of content stored in collections, automatically embedded and searchable via vector similarity. Used internally for context storage and retrieval.

Internal Metadata
Honcho’s internal state information that is not exposed through the API, used for system processing and state management.

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Background Processing

How Honcho processes data in the background to build intelligence.

Work Units
Discrete processing tasks that replace session-based processing. Work units handle multi-peer scenarios and different task types for more granular processing control.

Deriver Queue
Background processing system that derives insights from interactions, generates peer representations, and maintains context. Processes work units asynchronously.

Queue Status
Processing status indicators: Pending, In Progress, or Complete.

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Advanced Features

Powerful capabilities for sophisticated AI applications.

Batch Operations
Enhanced message operations including:

  • Batch message creation
  • Message querying with token and message count limits
  • Efficient bulk data processing

Scoped Search & Summary
Search and summary functionalities that can be scoped by:

  • Workspace level
  • Peer level
  • Session level

Context Retrieval
Session context retrieval with automatic summarization and intelligent token allocation management.

Peer Paradigm
A conversation model where AI entities act as peers rather than traditional assistants, enabling collaborative interactions and shared context development.

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Authentication & Security

Security and access control features.

Workspace-Scoped Authentication
JWT-based authentication system updated to support the workspace/peer/session hierarchy with appropriate access controls.

Scoped API Keys
Authentication tokens that can limit access to specific workspaces, peers, or resources for enhanced security and multi-tenant isolation.

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Technical Terms

Advanced technical concepts for power users.

These concepts require deeper technical understanding and are primarily for advanced implementations.

Vector Search
Finding similar content using cosine similarity between vector embeddings for semantic context retrieval.

Semantic Search
Search based on meaning rather than exact keyword matching, used for intelligent context assembly.

Token Allocation
Intelligent management of token usage across context retrieval, summarization, and response generation.

Multi-Tenancy
The ability to serve multiple isolated workspaces from a single Honcho instance with complete data separation.

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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.