Understanding and Managing Assistants in Gen AI Builder

What are Assistants

Assistants in Gen AI Builder are AI-powered agents that perform tasks, interact with users, access knowledge, and adhere to predefined behavioral guidelines. They are the central building block for creating interactive, AI-driven applications within the AI Factory ecosystem.

In short: Assistants turn your Knowledge Bases, Rulesets, and Tools into useful, conversational AI experiences.

For a deep dive, see: Assistants explained.

Why use Assistants

Assistants enable you to:

Build interactive AI applications quickly and reliably.
Ground AI responses in your organization’s data (via Knowledge Bases).
Enforce behavioral guidelines through Rulesets.
Enable advanced task execution through Tools.
Maintain conversational memory to support multi-turn interactions.

Assistants are the primary interface between end users and your AI Factory content + capabilities.

When to use Assistants

Whenever you want to expose AI capabilities to users — internal or external.
When building:
Support chatbots
Internal knowledge agents
Financial advisors
Sales assistants
Executive assistants
Compliance checkers
When building multi-modal Agents that combine:
Natural language conversation
Knowledge retrieval (RAG)
Behavior control
Task execution

How Assistants fit into Gen AI Builder

Typical Assistant flow:

User Input → Assistant → Retriever → Knowledge Bases → Retrieved Content → Rulesets → Behavioral Guidance → Tools → Action Execution (if configured) → Response Generation → User Output

At runtime:

The Assistant receives user input.
It retrieves relevant knowledge.
It applies behavioral Rulesets.
It uses Tools if needed.
It generates a response via its selected LLM.

Assistants provide a unified layer over all these components.

Key features of Assistants

LLM integration: Powered by your choice of Large Language Models (LLMs).
Knowledge Base connectivity: Link one or more Knowledge Bases for Retrieval-Augmented Generation (RAG).
Ruleset application: Enforce behavioral guidelines and tone.
Tool usage: (If configured) enable Assistants to perform external actions.
Conversation management: Maintain memory across interactions.
Customization: Tailor Assistant behavior, knowledge access, and generation parameters.

Getting started

See Create an Assistant for a full step-by-step guide.

Typical workflow:

Create an Assistant.
Select LLM model.
Add Knowledge Bases.
Add Rulesets.
Optionally configure Tools and Memory.
Test and deploy the Assistant.

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