This document is written based on FastGPT v4.17.0. It targets code and change review assistance scenarios for ad account opening audits, and outlines deployment configuration plans for multi-turn conversations and prompt engineering.
What data for this category looks like
Data for this category mainly comes from ad platform backends, the National Enterprise Credit Information Publicity System, customer-submitted account opening application materials, and industry access rules. Data updates occur daily or synchronously with each account opening application. Daily processed documents include account opening application forms, scanned business licenses, corresponding industry operation qualification documents, and historical account opening audit records. Data fields include the main body’s unified social credit code, advertising industry category, qualification validity period, audit node status, and so on. There are no unified fixed unit fields; format requirements only match the corresponding document types.
What constraints do these characteristics impose on the "multi-turn conversations and prompt engineering" link
Dispersed data sources updated per application require multi-turn conversations to bind session context to application batches, preventing information cross-interference across applications. Large format differences across data sources require prompts to clearly specify extraction field rules to adapt to content structures of different documents such as business licenses and industry qualifications. Single-application audit data has high timeliness requirements, so the valid scope of conversation context must be limited to prevent old data from interfering with current audit judgments. Audit verification relies on cross-validation of multi-source information, so multi-turn conversations must retrieve information from different sources in sequence to complete qualification verification and access judgment step by step.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique ID of the current account opening application | Ensure each audit’s conversation context is independent, avoiding information confusion across applications |
messages | Initialize per application batch, retain only historical messages related to the current audit | Adapt to the high timeliness of single-application data, reducing interference from irrelevant information |
variables | Inject global fields such as customer ID and industry category of the current application | Uniformly pass basic parameters required for audits, avoiding repeated entry |
| Maximum context rounds | First 3-5 rounds of conversation content | Control context length to adapt to the short-term and efficient processing needs of audit data |
| Prompt template | Clearly require extraction of fields such as unified social credit code and qualification validity period | Adapt to the field characteristics of this category’s documents, reducing information extraction errors |
| Conversation timeout | 600 seconds | Match the conventional processing duration of the audit process, avoiding audit interruption due to timeout |
Three common mistakes
- Phenomenon: Conversation nodes connected to the knowledge base return content that does not match the current account opening audit question. For example, asking about qualification validity period returns irrelevant material audit rules. Cause: The prompt does not clearly limit the conversation scope to only qualification-related questions for account opening audits, and the
variablesparameter is not bound to filter irrelevant knowledge base content. - Phenomenon: Conversation node returns
chat:LLM_model_response_emptystatus with no valid audit results. Cause: No reasonable context round limit is set, causing input tokens to exceed the model’s carrying capacity, or the prompt does not clearly specify the field range to be processed, triggering the model to produce no valid output. - Phenomenon: Global variables configured in the workflow are not correctly called by the prompt. For example, the industry category information of the current application is not obtained. Cause: The key name of the global variable is not correctly mapped in the
variablesconfiguration, or the prompt does not explicitly reference the variable placeholder, resulting in parameters not being injected into the conversation context.
How to confirm the configuration is complete
- Check if
chatIdis bound to the unique identifier of the current account opening application. Session logs can be viewed to confirm that session IDs for different applications do not overlap. - Trigger a test audit process to verify that the global parameters injected via
variablesare correctly referenced in conversation replies. For example, the reply includes the industry category information of the current application. - Simulate multi-turn conversations to confirm that the context only retains historical messages related to the current audit, with no information from other applications mixed in.
- Trigger a timeout scenario test to confirm that the conversation node returns results within the timeout period set by the audit process, and no timeout error is triggered.
The values listed are common starting points and should be evaluated against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.