Policy and Regulatory Basis Q&A for State-owned Capital Investment Companies: Multi-turn Dialogue and Prompt Engineering

What this category of data looks like

Data sources include state-owned capital investment and operation supervision documents published on the official website of the State-owned Assets Supervision and Administration Commission, internal business management systems issued by pilot state-owned capital investment companies, and local state-owned asset supporting regulations. Update rhythm adjusts with the release frequency of superior state-owned asset supervision documents. The update cycle of core policy documents adjusts dynamically per regulatory requirements. Document structures uniformly include fields such as issuing entity, document number, effective date, applicable level, clause classification, and compliance requirements. Single document lengths range from hundreds to tens of thousands of words. All fields use standard text formats, with no custom non-standard fields.

Constraints Imposed on Multi-turn Dialogue and Prompt Engineering

Fixed field requirements for official and internal policy documents require multi-turn dialogue to retain metadata such as issuing entity and effective date as traceability basis. This avoids confusion between old and new regulatory requirements. The clause-classified document structure requires prompts to guide the model to match queries clause by clause. Answers must not deviate from the document framework. The non-fixed update cycle feature requires multi-turn context to automatically filter expired document content. This prevents referencing invalid regulatory clauses. The wide range of single document lengths requires limiting the total length of single-turn context. This prevents exceeding model processing limits while ensuring key clauses are not truncated.

Configuration Settings

Reference only; verify against the target FastGPT release and deployment documentation.

Three Common Misconfigurations

• Phenomenon: Custom JSON format responses fail to parse correctly, resulting in messy returned formats. Cause: The JSON schema structure is not explicitly specified in the prompt, leading to mismatches between fields generated by the model and system requirements.
• Phenomenon: The running speed of the dialogue workflow for the same task is slower than in-workflow debugging. Cause: The number of context recall entries is not limited, resulting in loading excessive redundant policy documents with each call, increasing model processing time.
• Phenomenon: Auto-reply content is empty when opening a historical conversation for the second time. Cause: Conversation history is not correctly bound via chatId, leading to non-persistent storage of session context.

How to Verify Proper Configuration

• Initiate a compliance query. Check if the returned response includes the corresponding policy document number and effective date. This confirms the traceability requirement of the prompt has taken effect.
• Initiate multi-round follow-up questions. Check if subsequent responses expand based on the query intent of the previous round. This confirms the context binding function via chatId works normally.
• Import an expired state-owned asset policy document. Initiate a relevant query. This confirms the system automatically filters expired content and does not return invalid regulatory clauses.
• View the conversation history page. Confirm that all session contexts are stored classified by chatId, and previous conversation content can be loaded normally.

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 2026-09-28.