--- sourceDocument: Australia Conversational Interfaces sourceDocumentLink: https://www.servicenow.com/docs/r/australia/conversational-interfaces Release : - australia ft:locale : - en-US ft:publication_title : - Australia Conversational Interfaces ft:clusterId : - convint bundleId : - convint workflow : - Platform --- # Test details # Test details {#ariaid-title1} Release version: Australia Updated April 6, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Test details This content describes the testing interface and features available for Large Language Model (LLM) topics within ServiceNow's Virtual Agent Designer, specifically for the Australia release version updated April 6, 2026. It details the various tabs and functionalities that help you analyze, modify, and validate LLM conversational topics during development and testing. Show full answer Show less ## Key Features * **Analysis Tab:** Displays topic discovery results based on test inputs, showing matching skills, Knowledge Base articles, and catalog items when Genius Results and topic discovery are enabled. It supports semantic search for enhanced search recall and intent interpretation. * **Modify Instructions Tab:** Lists all instructions sent to the LLM from user input nodes, allowing you to edit, enforce, or revert prompts to optimize conversation flow. Changes can be applied and saved to the topic for further testing. * **Variables Tab:** Shows all conversation variables (input, script, Live Agent, and inter-topic variables) along with their current values, helping you track data flow during testing. * **Edit Variables Tab:** Available when testing topic blocks and custom controls, enabling direct editing of variables used in conversation nodes. * **Context Tab:** Allows specifying context variables to simulate different chat scenarios, influencing topic intent recognition and chat routing. This tab is not available when creating test cases. * **Logs Tab:** Provides a live log of server events, user inputs, and Virtual Agent responses to assist in troubleshooting and verifying conversation behavior. ## Practical Use for ServiceNow Customers When developing and testing LLM-driven Virtual Agent topics, you can leverage these tabs to: * Validate that the correct topics and assets are discovered and triggered by user inputs. * Refine LLM instructions for better conversation accuracy and control over user prompts. * Monitor and adjust variables to ensure data is captured and passed correctly throughout interactions. * Test different conversational contexts to simulate real user scenarios and routing conditions. * Review detailed logs to quickly identify errors or unexpected behavior during chat sessions. These tools collectively improve the efficiency and effectiveness of building intelligent, responsive virtual agents tailored to your organization's needs. The chat test window displays adjoining tabs that provide details about your topic as you test it. The following tabs are available when testing LLM topics: * Analysis: Results for topic discovery based on your input. * Modify instructions: List of all the instructions in the topic that are sent to the LLM. This tab is only available when testing a single topic. * Variables: List of all the variables used in the conversation, such as input and Live Agent variables. * Edit variables: Options for editing the variables used in the topic. * Context: Options for specifying the context (using context variables) in which a topic is run. * Logs: List of the processing performed. {#test-llm-details__ul_enb_f5c_tzb} The following tabs appear when you test their related topic types on the Virtual Agent Designer canvas: * The Analyze test phrases, Modify instructions, Variables, and Logs tabs appear for all LLM topic types. * The Edit variables tab appears for topic blocks and custom controls. * The Context tab appears for topics, setup topics, or small talk topics when you select Include topic discovery. {#test-llm-details__ul_hvg_zjr_tzb} ## Analysis tab {#test-llm-details__section_ob3_jpc_tzb} When you test LLM topics, results for topic discovery appear based on your input. When testing topics associated with a primary LLM assistant, only the primary assistant's promoted assets appear. When you input a test phrase, you can see a variety of search results when Genius Results are enabled including skill (topic) discovery, Knowledge Base (KB) articles, and catalog items. Under the skills search results, a Matching badge appears next to the skill discovered, while variables and values may also be listed (such as the variable @laptop_make and the value macbook), depending on the topic. These Genius Results only appear if you're testing a published topic and have selected the Include topic discovery option. If the Include topic discovery option appears inactive, publish the topic and select an LLM assistant on the Properties tab to work with topic discovery. A Search indexing in progress message might appear, but you can still test while the search indexing runs although topic discovery might not be updated. For more information about how Genius Results work, see [ServiceNow Otto for AI Search](https://www.servicenow.com/docs/access?context=now-assist-ais&version=australia&pubname=australia-platform-administration&ft:locale=en-US).{#test-llm-details__otherprops-int-1} Additionally, when testing active LLM assistant topics from the Topics page, you see skills results for Semantic search. Semantic search analyzes the meanings and context of your search terms and uses that information to find results with similar meanings. It improves search recall by interpreting natural language to more accurately reflect your search's intent. If semantic search is deactivated for topic discovery testing, those results are not displayed. For more information about semantic search, see [Semantic vector search in AI Search](https://www.servicenow.com/docs/access?context=semantic-search-ais&version=australia&pubname=australia-platform-administration&ft:locale=en-US). ## Modify instructions tab {#test-llm-details__section_ab3_wjc_tzb} If your topic contains any instructions that are sent to the LLM, they're shown in the Nodes category. The Nodes category lists LLM user input nodes on the canvas, their field names, and their contents. You can modify the content of any of the instructions to develop optimal instructions for the conversation. If a node has Enforce user prompt active, a blue check icon ![]() and a User prompt enforced message appears. Deactivate Enforce user prompt to modify the node's instructions. If any of your user inputs have Allow automatic slot filling activated, the input's Detail Description becomes a static field. If Allow automatic slot filling is inactive, you can define detail description using a script or data pill picker. For more information, see the Allow automatic slot filling table entry in [Text user input control](https://www.servicenow.com/docs/qrD7dUN8iEVcCSXmsoVg3w "The Text user input control in a Virtual Agent topic prompts the user for a text string.") or any other LLM user input controls. Each instruction's status starts at Original. If you alter an instruction's content, its status changes to Modified and a revert icon ![]() appears next to it. The total number of instructions modified is listed at the bottom of the tab next to a Save to topic button. To test the modified instructions, select Apply and restart. To undo changes to an instruction, select the revert icon next to the instruction. Alternatively, undo all changes by selecting Revert that activates when you modify any instructions. Note: If you select Save to topic, all changes are saved and all Modified messages reset to Original. The following image shows the Modify instructions tab showing user information with LLM instruction guidelines link, and Nodes held within an Input collector, including a locked node with User prompt is enforced message. The following image shows the Modify instructions tab with Modified instruction and Save to Topic updates. The Revert and Apply and Restart options activate when you make any changes. ## Variables tab {#test-llm-details__section_v2p_bxw_hmb} The Variables tab displays a list of all the variables used in the conversation and the associated values captured as the conversation progresses, so that you can follow along. A conversation can have these variable types: * Input variables * Script variables * Live Agent variables * Variables passed between a calling topic and topic block {#test-llm-details__ul_dz5_vyw_hmb}The list is separated into sections by variable types. The following example shows the Input variables section. Notice that for the static list control, both the display label and value are captured for the selected choice. The following example shows the Input variables section for the grouped list control. This variable information appears similar to the static list control, but the variables are separated by each group of the grouped choice. ## Edit Variables tab {#test-llm-details__section_rx5_p5c_tzb} When testing topic blocks and custom controls, you can edit the variables found in the nodes. ## Context tab {#test-llm-details__section_ubk_dbk_tzb} The Context tab appears when you're testing topics, setup topics, or small talk topics, to specify a different context for the chat. Choose a context variable from the list. The variables contain contextual information that can be used to determine topic intent or control how chats are routed to live agents. For example, you could select portal from the list of variables and enter the portal name IT Express. The Context tab is unavailable when creating test cases. For more information about defining context variables, see [Configure context variables for storing chat-related information](https://www.servicenow.com/docs/kGrAEmnoX4R6E0RV3rTFKw "Specify chat context variables, also called Live Agent chat variables, for storing chat-related information, such as pre-chat survey responses. These variables contain contextual information that can be used to determine topic intent or control how chats are routed to live agents. You can also define variables to capture contextual information passed in Virtual Agent topic scripts to share with live agents."). For more information about live agent variables that are included with Virtual Agent, see [Live agent chat context variables](https://www.servicenow.com/docs/auUlE1RZ6SBuydgrKzjiag "Use chat context variables to pass certain information from the topic to share with a live agent or to control how bot conversations are routed to live agents. Virtual Agent includes some default variables, and you can define new ones."). ## Logs tab {#test-llm-details__section_zl3_24g_dhc} The Logs tab displays a running log of server events, user entries, and Virtual Agent responses in the chat. You can review the logs to look for any important data about the chat including potential errors.