What Data for This Category Looks Like
Data for this category typically comes from financial systems, project management systems, contract ledgers, and requirements submitted by business departments. Daily produced documents include annual budget sheets, project proposal forms, project portfolio dashboards, and investment review reports. Data update cycles are mostly monthly, quarterly, or annual. Document fields usually include project number, budget amount, priority flag, revenue forecast, etc., with units such as ten thousand yuan, project cycle (months), etc. There is no unified standardized format, and formal documents must be generated by integrating data from multiple systems.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources lead to large context spans in single inputs. Effective context ranges must be limited to avoid interference from redundant information. For batch data with fixed update frequencies, the corresponding cycle dataset must be explicitly specified in dialogue to avoid confusion across cycles. Document fields are diverse and lack unified formats. Prompts must clearly require alignment with field definitions commonly used by business departments to ensure generated documents conform to business habits. For the requirement closing link involving multiple stakeholders, multi-turn dialogue must be used to gradually clarify vague requirement descriptions and reduce information deviation. Priority sorting logic involved in project portfolio management is complex. Prompts must include preset priority judgment rules to reduce manual adjustment costs.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context Length | 8000–10000 characters | Adapts to the dialogue length of multi-turn requirement clarification, and aligns with the context management limit of FastGPT v4.17.0 |
messages format | Stored in a structured "user question - assistant reply" format | Adapts to context recall for multi-turn dialogue, ensuring coherent dialogue logic |
variables scope | Limited to the current project team's session | Prevents confusion of cross-project budget data, meeting the project isolation requirements of this category |
chatId | Bound to the unique identifier of the current project team | Ensures multi-turn dialogue context is tied to the corresponding project, avoiding cross-project confusion |
| Data Source Binding | Bind preset variables from financial systems and project management systems | Aligns with the category's data sources, ensuring generated content references real business data |
| Session Timeout | 600 seconds | Adapts to the multi-turn communication rhythm of the requirement closing link, preventing premature session termination |
Three Common Misconfiguration Issues
- Phenomenon: After configuring multiple AI dialogue nodes in a workflow, the final chat interface displays reply content from all nodes. Cause: No hidden rules are configured for outputs from non-core dialogue nodes, leading to redundant context.
- Phenomenon: A single question cannot trigger multi-turn follow-up questions, and only a single reply is returned. Cause: The context retention configuration for multi-turn dialogue is not enabled, causing the session context to reset with each question.
- Phenomenon: Generated budget documents have missing fields or non-compliant formats. Cause: The prompt does not explicitly bind the category-specific document field rules, and does not align with the common formats used by business departments.
How to Verify Proper Configuration
- Launch a multi-turn dialogue for requirement clarification, and check whether previous questions and replies are retained in the context.
- Call bound data source variables to verify whether generated content references real data fields from corresponding systems.
- Configure multiple AI dialogue nodes, and test whether outputs from non-core nodes are not displayed in the final chat interface.
- Generate a test document, and check whether required fields such as preset project number and budget amount are included.
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.