What the Data for This Category Looks Like
Data for this category comes primarily from customer interviews, bidding documents, vendor product materials, and historical project proposals. Daily output includes technical proposal books, customer requirement survey forms, quotation lists, and demo scripts. Teams update data per individual pre-sales project, either per project or weekly. Document structures center on customer requirements, technical roadmaps, implementation boundaries, and quotations. Core fields include customer name, bidding number, proposal version, and requirement items. No unified fixed quantitative units exist. Adjustments follow each project’s specific requirements.
What Constraints Do These Characteristics Impose on Multi-Turn Dialogues and Prompts
Organizing data per individual project requires multi-turn dialogues to use a unique identifier. This prevents mixing data across projects. The weekly update frequency means context windows should not be too long. Otherwise, expired or irrelevant historical proposal content may load. Core business processes focused on requirement clarification and solution boundary design require multi-turn dialogues to focus on current project requirement items. Prompts must accurately match the current project’s unique fields. This avoids generating generic content unrelated to the current project. Additionally, the non-standardized structure of individual project documents requires prompts to flexibly adapt to different document types. This ensures review logic aligns with the current pre-sales proposal.
How to Configure Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Maximum context window | Retain the most recent 1200 characters of dialogue and associated document fragments | Aligns with the weekly update rhythm of individual projects, avoiding loading expired or irrelevant data |
| Session history filtering rules | Filter historical records by pre-sales project number | Achieve per-project session isolation to prevent cross-project data mixing |
| Prompt variable configuration | Inject customer name, bidding number, proposal version number | Allow prompts to accurately match the unique information of the current pre-sales proposal |
| Number of retained dialogue history entries | Only retain the most recent 15 valid dialogues | Control data transfer volume during API calls, improving response speed |
| Automatic question optimization | Enable automatic optimization of user questions | Adapt to the non-standardized question habits of pre-sales consultants, improving retrieval and review accuracy |
Three Common Misconfigurations
- Phenomenon: Calling the historical record interface returns session data for the entire application, unable to filter by project number. Cause: No session history filtering rule configured, or no unique project identifier bound, resulting in failed session isolation.
- Phenomenon: Dialogue response times out, or returns empty results. Cause: No maximum context window size limit configured, too many historical documents and dialogue fragments loaded, exceeding API processing limits.
- Phenomenon: Generated pre-sales proposal content is unrelated to the current project, with historical project information mixed in. Cause: No unique project variables injected, prompts cannot accurately match the current project’s requirements and boundaries.
How to Verify Successful Configuration
- Initiate a test dialogue, enter the preset pre-sales project number, check if the sessions returned by the historical record interface only include dialogue content for that project.
- Review the context window configuration, confirm that the loaded dialogue and document fragment length does not exceed the preset character limit.
- Check the prompt variable configuration, confirm that unique fields of the current project such as customer name, bidding number, etc., have been injected.
- Initiate multiple consecutive questions, verify that the system can continue the pre-sales proposal review logic based on historical dialogue content.
The values provided are common starting points and should be measured against the user’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.