What the data for this category looks like
Data for this category mainly comes from customer requirements, public data sources, collection tools, and in-house sample libraries. Update cycles follow single project timelines or weekly intervals. Four core document types are regularly associated with daily work: collection plans, sample specifications, authorization instructions, and task schedules. Data fields must clearly specify sample scope, collection fields, compliance identifiers, and delivery timelines. Common units include sample volume, number of data entries, and authorization periods. Data flow centers on project requirements. Core steps include sample scope definition, field specification confirmation, and collection legality verification. Unlike data cleaning and quality inspection, data collection task design determines how raw data is acquired. Multi-turn dialogue must focus on confirming collection rules.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources are scattered and linked to multiple document types. Multi-turn dialogue must synchronously associate historical context such as collection plans and sample specifications to avoid repeated questions about the same information. Update frequency adjusts as the project progresses. Prompts must support dynamic loading of the current project’s schedule and compliance requirements, and fixed templates cannot be used. Confirming sample scope and field specifications takes significant time. Multi-turn dialogue must use progressive questioning to gradually clarify boundaries and reduce repeated communication costs. Collection legality verification involves authorization instructions. Prompts must embed compliance verification logic to ensure dialogue output aligns with collection requirements. In FastGPT v4.17.0, configuration for this scenario can be precisely adapted by specifying parameters.
How to set the configuration
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Maximum Context | Previous 15 turns of dialogue or 8000–10000 characters or 20000 token | Matches the multi-turn communication needs of data collection task design, avoids core information loss caused by context overflow |
messages | Stored per project, linked to chatId | Ensures the dialogue history of the same collection project can be fully called, supports cross-link synchronization of communication records |
variables | Bound to the current project’s collection plan, sample specifications, and authorization instructions | Dynamically loads project-specific configurations, eliminates repeated input of fixed information, and adapts to dynamically updated project requirements |
| Single Search Limit | Top 3 relevant documents | Matches the document volume of collection tasks, avoids redundant information interfering with compliance verification and field confirmation |
| Prompt Optimization | Calibrated based on actual testing | Adapts to the professional terminology of collection tasks, improves the accuracy and professionalism of multi-turn dialogue |
Three common mistakes
- Phenomenon: Multi-turn dialogue repeatedly asks about sample scope or field specifications, leading to sluggish interactive experience. Cause: The project-associated storage logic for
messagesis not configured, causing context to fail to load correctly. - Phenomenon: The model outputs content that does not comply with collection compliance requirements, resulting in infringement risk prompts. Cause: The current project’s authorization description parameters are not bound via
variables, and legality verification logic is not embedded. - Phenomenon: After calling the deployed model, the response content deviates from the preset collection task goal. Cause: Exclusive prompts are not injected via
variables, and the prompts do not limit the response scope.
How to confirm the configuration is correct
- Initiate a simulated query to verify whether the dialogue context includes previously defined sample scope content.
- After calling the model, check whether the output complies with the current collection task’s compliance requirements and field specifications.
- Copy the
chatIdof the current session, call it in another tool, and verify whether the dialogue history can be loaded normally. - Check whether
variablesare bound to the current project’s collection plan and authorization description documents.
The values listed here are common starting points and should be evaluated against your own specific 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.