12 docs tagged with "conversational-ai"

How conversational AI creates artifacts such as reports, documents, images, and skills that you can preview in a side panel, revise with AI, and download.

A chat interface built into Atlan that lets you explore data, trace lineage, and look up business terms using everyday language.

A reference for everything you can do inside a conversational AI session—interacting with responses, response actions, managing history, and configuration.

Troubleshoot ignored instructions, degraded answers, replies without searching, and behavior differences across surfaces in conversational AI.

How custom instructions shape conversational AI answers organization-wide—what they can and can't do, patterns that work, anti-patterns to avoid, and how they compare to skills, memory, and personas.

Write custom instructions to shape how conversational AI answers questions across your organization—set vocabulary, preferred asset types, and response language.

Example prompts for exploring data, tracing lineage, generating SQL, and managing your catalog with conversational AI in Atlan.

How conversational AI remembers assets and context across sessions to give more relevant answers, and how that data is handled.

Find data assets using natural language questions with conversational AI. Search without filters or schema knowledge by asking about ownership, data quality, and lineage.

Learn how to enable and configure Atlan's conversational AI experience for your organization.

Where and how to use conversational AI—inside Atlan, in Copilot, in your data tools, in Slack, and in Microsoft Teams.

Atlan MCP use cases across chat-based AI tools (Claude, Cursor, ChatGPT, Gemini), automation platforms (Python, n8n, LangChain), and end-to-end workflows for metadata enrichment, governance, data engineering, asset lifecycle, and catalog adoption.