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DialogGPT is an agentic orchestration engine that powers natural conversations at scale. It autonomously manages intent identification, execution planning, and fulfillment across Dialog Tasks—without requiring training data. It uses text embeddings and generative models to identify user intents from task names and descriptions.
DialogGPT is the default intent identification mode for new AI for Service apps.

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Overview

DialogGPT handles the full conversation lifecycle in three phases: DialogGPT
  1. Chunk Shortlisting: Processes user input and conversation history to retrieve relevant chunks from a vector database
  2. Intent Identification: Analyzes retrieved chunks with an LLM to resolve intent, ambiguities, and execution order
  3. Flow Management: Triggers fulfillment (dialog tasks, FAQs, answer generation) and delivers contextually relevant responses

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Key Features

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Key Benefits

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How DialogGPT Works

DialogGPT processes each user utterance through three sequential steps.

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Step 1: Chunk Shortlisting

DialogGPT rephrases the user input to optimize retrieval, then uses a RAG pipeline to fetch relevant chunks—segments of dialog tasks, FAQs, or Search AI embeddings stored in a vector database. This retrieval operates independently of the Search AI pipeline.
From v11.15.1, only sub-intents relevant to the active dialog are appended to {{dialogs_chunks}}. Previously, all sub-intents were indexed with the top-level intent.
Intent Preconditions Filtering After Chunk Shortlisting, DialogGPT by default evaluates context-tag-based preconditions configured for intents and sub-intents in the shortlisted dialogs. Only dialogs that satisfy the configured preconditions are forwarded for final intent detection. Developers can trace this filtering through the Intent Preconditions node in DialogGPT NLP Insights and the Intent Preconditions Processing step in the Debug Logs.

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Step 2: Intent Identification

The Intelligent Conversation Orchestrator analyzes retrieved chunks with the user input and conversation context. Using an LLM, it:
  • Identifies the most appropriate intent.
  • Resolves ambiguities by ranking options or prompting the user for clarification.
  • Determines dependencies and execution order for multi-intent scenarios.
  • Routes system intents (repeat, pause, end) to pre-defined handlers.
Fulfillment is categorized as: single intent, multiple intents, ambiguous intents, or system intents.

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Step 3: Flow Management and Fulfillment

DialogGPT triggers the resolved intent and:
  • Executes dialog tasks or FAQs.
  • Generates answers for system intents using Answer Generation.
  • Requests clarification for ambiguous intents.
  • Handles multi-intent scenarios sequentially or in parallel based on dependencies.
Responses adhere to enterprise business rules and the configured interaction mode (text or voice).

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Configuration

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Conversation Types

Configure which conversation types DialogGPT handles. Dialogs and FAQs are enabled by default and can’t be disabled. To manage: Settings > toggle on/off > Save. Configuration

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Model Configurations

Use the Model Configurations card to set up models for chunk shortlisting and conversation management. Shortlisting Relevant Chunks: BGE-M3 is the supported and recommended embedding model. The embedding model settings apply only to Dialogs and FAQs; for Knowledge from Search AI, the settings in the Search AI app apply. Optionally adjust Similarity Threshold and Proximity Threshold in advanced settings. Defaults work for most cases. Maximum Chunks from Search AI: Set the maximum number of chunks shortlisted from Search AI and sent to the LLM for answer generation. Default: 5. Click Go to Search AI to configure retrieval settings. Conversation Management: Select the LLM for intent detection and execution planning. Supported models: Advanced settings: Temperature, Max Tokens, Conversation History Length. Defaults are sufficient for most cases. To configure: Settings > select model and prompt > adjust settings > Save.

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Fulfillment

Fulfillment defines how DialogGPT responds to identified intents. There are two event types: Intent Events and Conversation Events.

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Intent Events

To configure: click the event’s Settings icon > define event configuration > toggle on/off > Save.

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Conversation Events

Conversation Events activate when Conversation Intents is enabled. They handle common conversational phrases. To configure: click the event’s Settings icon > define configuration > toggle on/off > Save.

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Enabling DialogGPT

Prerequisites: Integrate the LLM that will power DialogGPT. See Model Configurations and LLM Integration. Steps:
  1. Go to Generative AI Tools > DialogGPT.
  2. Click Get Started.
  3. Select the Conversation Types to enable.
  4. Under Model Configuration, select the embeddings model for Dialogs and FAQs.
  5. (Optional) Click Show Advanced Settings to adjust Similarity Threshold and Proximity Threshold.
  6. (If Knowledge from Search AI is selected) Set the maximum number of chunks to shortlist from Search AI. Click Go to Search AI for retrieval settings.
  7. Select the Conversation Management Model and Prompt.
  8. (Optional) Adjust Temperature, Max Tokens, and Conversation History Length (The maximum number of recent messages that can be sent as context is 50).
  9. Click Enable DialogGPT.
To disable: Click the More icon (⋯) in the top right > Disable DialogGPT > confirm. Disabling reverts intent detection to traditional NLU.

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Debug Logs

Debug Logs provide step-by-step visibility into DialogGPT’s decision-making and execution flow. Every LLM call includes full request and response details. For each user utterance, logs capture: Access Debug Logs via the Playground.

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FAQs

Does DialogGPT require training data? No. DialogGPT uses zero-shot intent detection via pre-trained embeddings and LLMs. Developers provide concise dialog descriptions and FAQ alternate questions—embedding models handle the rest. How does zero-shot understanding work? Pre-trained embeddings process the query and conversation history to identify intents and generate accurate responses for unseen scenarios, without any prior training examples. How does DialogGPT handle multiple intents in one query? It uses LLMs to parse multi-intent queries and executes intents in parallel where possible. For example, “Check the weather and book a flight” triggers both tasks simultaneously. How does DialogGPT resolve ambiguous queries? It ranks ambiguous options and either presents them to the user for selection or prompts for clarification before proceeding. How does DialogGPT handle FAQs? FAQs are treated as primary resources. User inputs are processed against FAQ chunks in parallel with dialog intents and search queries, using embeddings for retrieval. How does DialogGPT handle conversational interruptions? It supports conversation intents for common interruptions, including:
  • Repeating information
  • Holding or skipping tasks
  • Handling digressions (for example, off-topic questions mid-dialog)
What role does query rephrasing play? DialogGPT rephrases queries to optimize chunk retrieval. For example, “What about Hyderabad?” is rephrased with conversational context before processing, improving retrieval accuracy. Can DialogGPT be tuned for domain-specific use cases? Yes. Customize dialog descriptions, FAQs, and embeddings to suit the domain. The modular architecture supports adaptation across industries. Does DialogGPT support small talk? Yes. Small talk is treated as a conversation intent, making interactions feel more natural and less robotic. Can existing apps be migrated to DialogGPT? Migration is supported. Transitioning from XO10 to XO11 is required; apps using Smart Assist may need additional configuration. How does DialogGPT compare to traditional NLP? What happens if no intent is matched? DialogGPT routes the input to Search AI or a predefined fallback dialog—no dead ends. Can DialogGPT handle fallback scenarios? Yes. If a user input doesn’t match any intent or FAQ, DialogGPT intelligently routes it to Search AI or a predefined fallback dialog. Is DialogGPT enterprise-ready? Yes. DialogGPT supports enterprise-grade security, including secure integrations with cloud providers like Azure (for example, exclusive data protection agreements for regulated industries).