This document is compatible with FastGPT v4.17.0.
What Data for This Category Looks Like
Core data for this category comes from ticketing systems, customer interview records, CRM systems, and product roadmaps. Daily outputs include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning documents. Data updates weekly or per release cycle. Fields include requirement source, priority, associated customers, estimated development hours, and more. There are no unified fixed units; adjustments are needed based on specific requirement scenarios. The primary use case is filtering and sorting multi-customer feedback, separate from subsequent requirement analysis and PRD management steps.
What Constraints Do These Characteristics Impose on the Context and Token Workflow
Data sources for this category are scattered, with significant format differences. Document lengths and field complexity vary across sources, which demands precise context slicing. Data updates weekly or per release cycle. Context must be synchronized regularly to match the latest requirement status. Outdated requirement information will appear otherwise. Requirement pool data includes segmented fields such as associated customers and priority. Only recalling general context cannot support precise requirement evaluation and scheduling communication. This easily leads to redundant or missing context, increasing unnecessary token consumption.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 12000–15000 token | Adapts to the mixed content volume of multiple documents in the requirement pool, avoiding exceeding the model context window |
| Chunk Overlap Length | 200–300 characters | Ensures context coherence of associated fields in the requirement pool, preventing requirement information from being broken |
| Single Recall Limit | Top 5–7 matching documents | Matches the number of fields in requirement pool data, keeping token consumption within a reasonable range |
maxContext | 12000–15000 token | Aligns with the maximum context parameter set in the interface, unifying the context carrying limit |
| Context Recall Threshold | 0.72–0.85, calibrated via actual testing | Accurately filters historical records related to current requirements, reducing interference from irrelevant customer feedback |
Three Common Misconfigurations
- Phenomenon: The context recalled from the knowledge base is unrelated to the current requirement, resulting in off-topic responses. Cause: The recall threshold is not adjusted for the multi-field characteristics of the requirement pool, and irrelevant customer feedback is included in the context.
- Phenomenon: Context cannot be carried when calling workflow APIs, or context is lost after consecutive follow-up questions. Cause: Context synchronization configuration is not enabled in workflow nodes, or historical documents from the requirement pool are not bound to the session context.
- Phenomenon: An error indicating context overflow is prompted after document upload, and chunking cannot be completed. Cause: Chunking parameters are not adjusted based on the average length of requirement pool documents, causing single chunk content to exceed the token limit supported by the model.
How to Confirm Proper Configuration
- Upload a complete requirement pool list document, check if the chunked content retains coherence of core fields such as customers and priority.
- Initiate two consecutive follow-up questions, confirm that the second question can associate the requirement context from the first question, with no information breakage.
- When calling workflow APIs, check if the session context identifier is included in the request parameters, and if the returned results match historical records from the requirement pool.
- View the
maxContextparameter value on the configuration page, confirm it does not exceed the context window limit of the large model currently in use.
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 2026-10-19.