Debugging AI Bots: How to Inspect and Correct Response Info in HighLevel

When an automated AI conversation bot misinterprets customer questions or provides inaccurate business details, troubleshooting can become frustrating without visibility into the bot's decision-making process. Attempting to adjust system prompts globally without knowing why a specific failure occurred often leads to trial-and-error edits that solve one problem while creating others.

HighLevel addresses this lack of visibility through its native AI Response Info tool. By allowing agency operators to inspect the specific context, intent, and knowledge sources used to generate individual responses, teams can audit logic and resolve errors directly from the conversation inbox.

Locating AI Response Metadata in the Inbox

Analyzing an automated output begins directly within the lead's thread. Rather than navigating away to general account settings or assistant configurations, operators can review technical metadata on a per-message basis.

1. Navigate to the Conversations view within the designated sub-account.
2. Select the contact thread containing the AI-generated message that requires review.
3. Locate the specific response sent by the bot and click the small AI icon displayed adjacent to the message bubble.

Selecting this icon slides open the AI Response Info panel, transforming the standard inbox view into an active diagnostic workspace for that specific interaction.

Understanding the Three Core Components of Response Info

The inspection sidebar breaks down the output generation process into three distinct operational layers. Evaluating these layers helps identify whether an issue stems from system instructions, classification rules, or source data.

• Prompt: The base instructions and rule constraints active when the specific message was constructed.
• Intent: The primary goal or categorization assigned to the incoming message from the lead.
• Sources: The exact knowledge base chunks, FAQ items, or web pages referenced to pull accurate facts.

Editing Knowledge Base Sources and Instructions in Real Time

Once an error is identified within the sidebar, operators can make corrections immediately. Updating information within this drawer saves changes directly back to the active configuration, preventing the bot from repeating the error in future conversations.

Operational adjustments can be applied across several areas directly from the panel:

• Source Knowledge: Correct inaccurate pricing, altered business addresses, or outdated service lists directly within the source text editor.
• Prompt Guidance: Refine boundary instructions or tone rules inside the prompt text area to adjust behavioral constraints.
• Intent Mapping: Navigate to global Conversation AI settings if the classified intent requires re-mapping to a different response flow.

Establishing Quality Control Routines for Agency Accounts

Managing automated conversational workflows across multiple client accounts requires structured quality assurance protocols. Waiting for clients or leads to report inaccurate information creates unnecessary risk for agency retention.

Implementing a standard auditing workflow ensures that bot training keeps pace with account updates:

1. Establish regular audits of active conversation logs, focusing on new sub-accounts or recently updated offers.
2. Inspect flagged or unbooked threads using the AI Response Info panel to check source context and prompt handling.
3. Update snapshot master templates whenever recurring knowledge gaps or intent errors are identified.