What the data for this category looks like
This category of data primarily originates from raw datasets, ETL operation logs, annotation platform outputs, and sampling quality inspection results. Update frequency is daily or synchronized per data batch. Core daily deliverables include cleaning rules, quality inspection sampling tables, abnormal data lists, and delivery reports. Document fields typically include data identifiers, exception types, processing statuses, associated batch numbers, and other related items. Common units are data counts, timestamps, and exception proportion percentages.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Raw dataset batches have significant differences. Batch context must be synchronized during multi-turn dialogue to avoid confusion between cross-batch rules. The daily or per-batch update frequency requires the dialogue flow to support batch document association, preventing processing of expired data. Document fields include key information such as exception types and processing statuses. Prompts must explicitly require extraction of these corresponding fields; otherwise, the generated requirement documents will lack validation items. The time-series nature of ETL logs requires retaining operation chain context during dialogue to facilitate backtracking of the execution basis for cleaning rules.
Configuration settings
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables bound fields | Data batch number, exception type, processing status | Associate category-specific data fields to ensure batch and exception association information is passed during multi-turn dialogue |
messages history turn limit | Top 10 dialogue turns | Limit context redundancy to avoid irrelevant cross-batch history interfering with current requirement drafting for cleaning rules |
chatId session isolation rule | Bind per data batch | Ensure that dialogue contexts for requirements and design documents of the same batch are not confused with those of other batches |
| Single dialogue return limit | Top 5 abnormal data entries | Match the conventional sampling volume of quality inspection sampling, avoiding excessive data returned in a single dialogue that causes timeout |
| Timeout threshold | 600 seconds | Adapt to the processing time of ETL logs and batch data, avoiding timeout errors triggered by large data volumes |
| Maximum context length | 800–1200 characters | Match the conventional length of single documents such as cleaning rules and quality inspection sampling tables, avoiding truncation of key fields |
Three common misconfigurations
- The interface displays a request timeout or returns a 504 status code. This occurs when no timeout threshold adapted for batch data processing is set, and pagination is not implemented for abnormal data lists.
- Unconfigured knowledge base content is automatically included in dialogue. This happens when the global knowledge base association switch is not turned off, leading to irrelevant external data being mixed into requirement drafting.
- It is not possible to set a single dialogue turn to trigger a prompt only once. This is caused by failing to lock the prompt call count in session configuration, resulting in repeated triggering of the prompt generation process.
How to confirm configurations are correctly set
- Import a single batch of cleaning rule documents, initiate the first dialogue, and verify that the context only includes field information for that batch.
- Simulate a query for batch abnormal data, wait for the dialogue response, and confirm that the response duration does not exceed the set threshold.
- Trigger one prompt call, and check that subsequent dialogues do not repeatedly trigger the same prompt generation logic.
- View the
chatIdsession list, confirm that dialogue contexts for different data batches are isolated, with no cross-batch data confusion.
The values provided 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.