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The Ask Block is an LLM-powered component designed to ask a single, specific question to the user. It simplifies your conversation workflow by dedicating one optimized block to one specific task. Instead of writing complex logic chains, the Ask Block utilizes a pre-trained model to handle user inputs, understand context, and drive the conversation forward.

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Key Components

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1. Prompt Section

This is the core instruction for the Voice AI Agent. Because the block is pre-trained, you do not need to provide a complex system prompt. Simply input the specific question you want the agent to ask. The system will automatically convert this into a highly optimized Voice AI prompt. Example:
  • Do: “What is the size of the apartment you are looking to buy?”
  • Don’t: “You are a real estate agent. You need to ask the user about the apartment size. If they say small, ask…“

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2. Add Condition

This optional setting acts as a “Gatekeeper” for the conversation flow. It defines the logic required for the agent to move to the next node.
  • How it works: If the condition is not met, the agent will loop on this node until the user provides the required information.
  • Example: “The condition is achieved if the user has stated their apartment size requirement in sqft.”

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3. Finetuning

Use this section to train the AI on specific edge cases to improve model accuracy. You can provide “Few-Shot” examples (conversation history + expected agent response).
Finetuning is highly recommended if your question involves industry-specific jargon or if you expect ambiguous answers from users.

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Settings & Configuration

The Settings section provides granular control over the AI Agent’s behavior, specifically regarding latency, interruptions, and fallback logic.

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Retry Logic

“After how many tries should the bot exit the block?” This setting prevents the agent from getting stuck in an infinite loop.
  • Function: If the user fails to provide the required information after the specified count, the agent will “hard exit” the block and follow the Fallback Node connection.
  • Use Case: Ideal for optional information collection. If the user declines to answer multiple times, the agent can gracefully move on.

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Conversation History

Enable Conversation History allows the agent to remember context from earlier in the call.
  • Enabled: The agent delivers a personalized experience referencing previous answers.
  • Disabled: Useful for mandatory disclosures or legal statements where the script must be followed exactly without being influenced by prior context.

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Barge-in (Interruptions)

This control determines if the user can interrupt the agent while it is speaking.
  • Enabled (Recommended): Creates a natural, humane conversation flow.
  • Disabled: Use this for compliance statements or critical instructions where the agent must finish speaking. User audio during this time is ignored by the flow logic but is still captured in the transcript.

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Nudge

Controls how the agent handles extended periods of silence.
  • Default: The agent repeats the question in a slightly altered way to re-engage the user.
  • Disabled: The agent will wait indefinitely (or until global timeout) if the user does not speak.

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AnswerFlow

AnswerFlow allows the Ask Block to leverage your knowledge base (RAG) to answer user queries that might arise during the block’s execution.
  • Document Selection: Select specific tags (e.g., refund, credit-card) to limit the knowledge base scope. If left blank, the entire library is used.
  • Response Formatting: Define the persona or format for the answer (e.g., “Short and concise,” “Descriptive”).
Latency Alert: Enabling AnswerFlow requires the agent to search and retrieve documents, which will add latency to the response time. Use only when necessary.

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Speech Normalization

Converts structured data (numbers, dates, units) into natural speech text. You can choose from the list of normalization options supported.
  • Example: Converts “1000sqft” to “one thousand square feet.”
Latency Alert: Only add normalizations required for the specific block. Adding unnecessary normalizations increases processing time and latency.

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Exit Condition & Variable Extraction

These conditions determine when the agent successfully leaves the block. You can also extract variables (slots) from the conversation here.
Performance Best Practice: Extracting variables at this stage leads to higher latency.We recommend avoiding variable extraction within the Ask Block unless that data is immediately required in the very next step of the flow. For data analysis, utilize Post-Call Insights to extract information after the call concludes.