What the data for this category looks like
Data for this category comes from call recording transcripts of government hotlines and state-owned enterprise customer service, and original ticket follow-up records. The update cadence is generating a corresponding summary once a single follow-up is completed, and batch syncing to the knowledge base by daily ticket batches. Each document includes fields such as call time period, agent identification, user core request, processing progress, follow-up conclusion, and satisfaction score. The call time period uses the YYYY-MM-DD HH:MM:SS standard time format. The satisfaction score is an integer between 0 and 5. The agent identification is a string type.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
This category of data includes colloquial call transcripts and multi-field structured information, which imposes multiple constraints on the multi-turn dialogue and prompt engineering link. First, colloquial transcripts contain a large amount of redundant expressions. Prompts need to add pre-formatting instructions to unify expression formats. Second, each single document includes multiple types of fields. The messages parameter needs to limit context rounds to avoid non-core fields interfering with dialogue logic. In addition, for the batch sync update cadence, prompts need to limit the processing scope to the follow-up records of the current ticket batch to prevent cross-ticket data confusion. Differences in field units also require clear parsing rules in prompts to avoid parameter parsing errors.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages context limit | Only retain the first 3 turns of dialogue for the current ticket | The content of each single follow-up record summary is concentrated. Excessive context will dilute core request information |
| Single dialogue token limit | 8000 token | The length of each single follow-up record summary mostly falls within the 1000-3000 character range, calibrated based on actual testing |
| Prompt template | Include a pre-instruction of "first standardize colloquial transcript content, then extract core requests and conclusions" | The data source is call transcripts with a large amount of colloquial redundancy |
variables parameter | Bind current ticket ID and follow-up date | Need to limit processing scope to current batch's follow-up records to avoid cross-ticket confusion |
chatId association rule | Bind single follow-up record ID | Need to ensure multi-turn dialogue only targets a single follow-up record to prevent context interference |
| Question optimization switch | Enable | Per FastGPT v4.17.0 dialogue module optimization rules, can improve parsing accuracy of colloquial content |
Three common mistakes
- Phenomenon: A 500 error is returned when importing follow-up record summary documents. Cause: Single documents exceeding length are uploaded without following the daily batch format, causing system parsing timeout.
- Phenomenon: After multiple AI dialogue modules run in a workflow, the output of all modules is returned. Cause: The
chatIdis not used to bind a single follow-up record, so context is not isolated, and multi-turn dialogue results are superimposed. - Phenomenon: The dialogue module takes more than 3 seconds to return a result on the first call. Cause: The context rounds of
messagesare not limited, so the system loads all historical dialogue data each time, increasing initialization time.
How to confirm the configuration is correct
- Upload a single follow-up record summary document, confirm there are no error prompts on the interface.
- Launch a multi-turn dialogue test, verify that only content related to the current follow-up record is returned, with no cross-ticket information.
- Check that the
variablesparameter is bound to the current ticket ID and follow-up date to ensure the processing scope is correct. - Test the first call to the dialogue module, confirm that the initialization time matches the preset actual calibration value.
The values provided are common starting points and should be measured 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 2026-09-28.