Advanced Settings - Kore.ai Docs

The Advanced Settings page configures advanced execution parameters for a project. Use it to change the project slug, control LLM reasoning behavior, customize the system prompts that drive data collection, edit escalation messages, snapshot settings versions, and delete the project. Navigation: Project -> Settings -> Advanced

Slug

The slug is the URL-safe identifier for the project. The platform uses it in project URLs and in any endpoint or integration that references the project by name.

Field	Description
Slug	The project identifier. Edit the value and click Save to apply the change.

Editing the slug breaks existing URLs registered in third-party systems. Update every webhook, channel configuration, and external integration that references the old slug before you save.

LLM thinking

Thinking controls whether the LLM produces explicit reasoning before it calls a tool. Reasoning improves traceability in session traces, but it consumes additional tokens on every tool call.

Setting	Description	Default
Enable Thinking	When enabled, the LLM provides detailed reasoning (thought) for each tool call. This improves traceability but increases token usage.	Disabled
Thinking Budget	Maximum number of tokens the LLM can spend on reasoning. Accepts a value from 128 to 32,768.	1,024
Thought Description	Instruction that the platform appends to tool descriptions to guide the LLM’s reasoning output. Use it to shape what the model explains, such as why it chose a particular action.	Empty

Thinking Budget and Thought Description apply only when you enable Enable Thinking. A higher budget produces longer reasoning traces and raises per-call token cost.

LLM task prompts

The platform uses a set of system prompts to handle entity extraction, correction detection, validation, and inference during data collection. Each prompt ships with a default. Expand a prompt row to read the default text, then edit it to change how the model behaves for that task in this project.

Prompt	Description
Entity Extraction Prompt	System prompt for extracting field values from user messages during data collection.
Correction Detection Prompt	Detects when a user corrects a previously provided value. The prompt receives the currently collected values as context.
Field Validation Prompt	Validates extracted values against field rules and constraints before the platform accepts them. The prompt returns a JSON object that reports whether the value is valid.
Field Inference Prompt	Infers the most likely values for missing fields from the collected context. The prompt returns a value only when confidence is high.

The platform marks Entity Extraction Prompt as deprecated with inline gather. Projects that use inline gather don’t rely on this prompt. Engineering needs to confirm the replacement mechanism and whether the prompt still applies to projects built before inline gather.

Edit these prompts only when the default behavior doesn’t fit your data collection flow. Changes apply to every agent in the project.

Escalation templates

Customize the escalation confirmation messages that users see when a conversation hands off to a human agent. Each channel type has its own template:

Channel	Description
Digital Channel	Appears in web chat and messaging apps. Supports markdown formatting and emoji.
Voice Channel	TTS reads it aloud on voice or phone channels. Keep it short and natural.
Plain Text	Fallback for SMS and channels without rich formatting support.

Each template supports {{reason}} and {{priority}} variables that the system substitutes at runtime with the actual escalation reason and priority level.

Danger zone

The Danger Zone section permanently deletes the project and all its agents, tools, deployments, and settings. You can’t undo this action. Click Delete Project to proceed.

Settings versions

After you change a settings and save it, you can create a versioned snapshot of all project settings. Click Create Version to save the current settings state. These versions enable deployment pinning, allowing you to associate a specific settings version with a deployment environment. For example, you can keep tweaking configurations and promote the versioned snapshot to staging deployment to test the project. Once satistied with the configuration, you can promote the specific versioned snapshot to active deployment. When you export a project, you can include or exclude the version history.

​Slug

​LLM thinking

​LLM task prompts

​Escalation templates

​Danger zone

​Settings versions

Slug

LLM thinking

LLM task prompts

Escalation templates

Danger zone

Settings versions