---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: b2053a352c9b3d4d1c6f09883489fcc45fd3a2986739d82107e31875352841a0
  pageDirectory: concepts
  sources:
    - openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - grounded-and-topical-ai-applications
    - Topical AI Applications and Grounded
    - GATAA
  citations:
    - file: openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md
title: Grounded and Topical AI Applications
description: A mitigation requirement that AI chat and conversational agents must be grounded or topical, meaning users cannot freely query the model for unrestricted outputs.
tags:
  - AI-safety
  - conversational-AI
  - guardrails
timestamp: "2026-06-19T19:50:15.608Z"
---

# Grounded and Topical AI Applications

**Grounded and Topical AI Applications** refer to conversational AI systems where user interactions are deliberately constrained to a specific domain or subject matter, preventing unrestricted access to query the underlying model with general inputs that could generate unrestricted outputs. This design pattern is a required mitigation for certain high-risk use cases involving models like OpenAI's GPT series when deployed on Databricks. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

## Overview

When deploying applications that involve chat or conversations with a conversational agent, organizations must ensure that the application is "grounded or topical." This means users interacting with the application do not have unrestricted access to query the model with general inputs that generate unrestricted outputs. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

The grounding or topical requirement applies specifically to high-risk use cases involving:
- Applications involving chat or conversations
- Applications that enable users to interact with a conversational agent

## Key Characteristics

A grounded or topical AI application has the following characteristics:

### Constrained Domain

The application is limited to a specific subject matter or domain. Users can only ask questions or make requests that fall within the intended scope of the application. This prevents the model from being used for unintended purposes or generating inappropriate content outside the application's design. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

### No Unrestricted General-Purpose Access

Users cannot query the underlying model with arbitrary, open-ended inputs. The application architecture ensures that all queries are routed through filters, guards, or domain-specific logic that constrains what the model receives and produces. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

## Relationship to Other Mitigations

Grounded and topical design often works alongside other mitigation requirements. For example, applications accessible to users outside an organization must also implement authentication or monitoring mechanisms such as two-factor authentication, logging of individual end user IDs, or logging of individual IP addresses for visibility and remediation. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

## Applications with Image Inputs

A related but separate requirement exists for applications enabling image inputs. When using OpenAI models on Databricks, organizations must ensure that image inputs are restricted to low risk and topical images. ^[openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md]

## Implementation Considerations

Implementing a grounded or topical application typically involves:
- [Guardrails](/concepts/guardrails-ai-framework.md) — Input and output filters that constrain model behavior
- [Retrieval Augmented Generation (RAG)](/concepts/retrieval-augmented-generation-rag.md) — Grounding model responses in a specific knowledge base
- Prompt Engineering — Designing prompts that keep the model within a specific domain
- Model Serving Security — Configuring deployment settings to restrict model access patterns

## Related Concepts

- OpenAI High-Risk Use Case Mitigation Requirements
- Responsible AI
- AI Safety
- [Model Governance](/concepts/ai-governance.md)

## Sources

- openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md

# Citations

1. [openai-high-risk-use-case-mitigation-requirements-databricks-on-aws.md](/references/openai-high-risk-use-case-mitigation-requirements-databricks-on-aws-1a6b8630.md)
