What the data for this category looks like
Data sources include monitoring systems, ticketing systems, contract documents, and customer communication records. Update frequency is monthly or quarterly. Generated routine documents include SLA clauses, monthly service reports, availability statistics, and customer review minutes. Document fields include service cycle, number of abnormal events, response time range, contractually agreed metrics, and more. There are no fixed uniform units; values must match the business scenario of the corresponding document. Document fields may vary across different customers.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source data sources require multi-turn dialogue to support distinguishing context from different data sources, to avoid mixing monitoring data with contract terms. Monthly or quarterly update frequency requires dialogue to support filtering data by cycle, to prevent pulling historical records outside the current cycle. Differentiated fields across multiple document types require prompt templates to adapt to the format and field definitions of different documents, to ensure consistent terminology. The most time-consuming indicator calculation and attribution process requires multi-turn dialogue to guide users to clarify calculation rules and attribution scope, to reduce manual revision costs. Consequences of errors require results generated by dialogue to strictly match contractually agreed terms, to avoid content that conflicts with contract clauses.
How to set configurations
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Top 10 historical conversations | Adapts to monthly/quarterly low-frequency update business scenarios, avoids context overload |
| Variable Injection | Bind contract, ticketing, and monitoring data variables | Matches multi-source data requirements for document fields |
| Historical Dialogue Grouping | Store grouped by chatId | Distinguishes service report context across different customers, prevents data mixing |
| Cycle Filtering | Filter data by natural month/quarter | Matches the monthly/quarterly update frequency of this category |
| Prompt Template | Pre-set SLA terminology alignment rules | Reduces time spent manually adjusting report terminology |
Three common misconfigurations
- Symptom: Dialogue triggers incorrect SLA indicator calculation workflows. Cause: Failed to bind the corresponding customer’s contract variable in multi-turn dialogue, leading to use of incorrect service cycle parameters.
- Symptom: Dialogue returns no data or empty results. Cause: Cycle filtering rules are not configured, leading to attempts to pull historical records outside the current month/quarter.
- Symptom: An extra historical record appears after API calls. Cause: Context truncation rules for the
messagesparameter are not set correctly, leading to repeated submission of initial prompt entries.
How to verify proper configuration
- Enter the test customer’s contract number, verify that the SLA indicators called in the dialogue match the agreed fields of that contract.
- Trigger the monthly service report generation workflow, verify that the time range of returned data matches the current natural month.
- Call the API to initiate a dialogue, check that the returned
messagesparameter contains no duplicate initial prompt entries. - Test the abnormal event attribution function, verify that results are associated with corresponding ticketing system records.
The values listed here 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.