What Data Looks Like for This Category
Data for this category originates from ticketing systems, customer interview records, CRM systems, and product roadmaps. Update frequency is weekly or per version release cycle. Daily produced documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Typical fields include requirement ID, customer identifier, requirement details, priority level, evaluation cost, and scheduling status. There is no unified mandatory format, with minor adjustments based on team configuration.
What Constraints Do These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
Decentralized data sources require multi-turn dialogue to first associate requirement data across multiple systems, to avoid calling outdated or incorrect requirement pool information. The periodic update frequency means dialogue context must be synchronized with the latest requirement pool data regularly, to prevent processing outdated requirement content. The inconsistent document format requires prompt engineering to clearly define field extraction rules, to ensure standardization of requirement information. Duplicate requirement merging and value evaluation are core time-consuming tasks; multi-turn dialogue prompts should prioritize these tasks to reduce processing of non-core content. Multi-turn dialogue must maintain the messages parameter to retain context for historical requirement merging, avoiding repeated processing of the same requirement entries.
Configuration Guidelines
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Reference Content Template | a template containing the requirement ID, customer name, requirement details, and priority level fields from the actual source schema | Matches the standard field format of requirement pool lists, ensuring complete extracted requirement information |
| Reference Template Prompt | Answer based on the currently associated customer requirement pool data, prioritize completing duplicate requirement merging and value evaluation | Clarifies the core task of the prompt, aligning with the category’s business scenario |
variables | {"current_pool": "当前会话绑定的需求池ID"} | Binds context data for the corresponding requirement pool to accurately call the correct data source |
| Single Recall Count | Top 3-5 entries | Controls context load to avoid exceeding model processing limits |
| Maximum Context Length | 8000-12000 characters | Adapts to the conventional context carrying capacity of mainstream large language models |
Three Common Configuration Mistakes
- Phenomenon: The requirement data returned by the dialogue does not match the currently configured requirement pool, with missing fields or irrelevant content. Cause: The context identifier for the corresponding requirement pool was not bound via
variables, leading to calls to an incorrect data source. - Phenomenon: Knowledge base references are configured, but requirement pool data from the knowledge base is not correctly used in dialogue. Cause: The roles of the reference content template and reference template prompt are confused, and requirement pool data was not correctly injected into the prompt context.
- Phenomenon: Dialogue responses experience content truncation or timeouts. Cause: The set maximum context length exceeds the model’s carrying range, and the single recall count is not limited, resulting in excessive incoming context data.
How to Verify Successful Configuration
- Manually enter a test requirement, check if the dialogue return result includes the corresponding fields and priority information of that requirement.
- Initiate a multi-turn dialogue to test whether the duplicate requirement merging logic meets business expectations, adjust the single recall count and context length as needed.
- Check whether the
variablesparameter is bound to the requirement pool ID corresponding to the current session to ensure correct data source calls. - View the dialogue log to confirm that the reference content template and prompt take effect as configured, with no missing fields or incorrect splicing.
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.