What This Category of Data Looks Like
Policy and regulatory basis data for the shipbuilding sector is primarily sourced from official standardized documents, industry standards, and regulatory requirements issued by the Ministry of Industry and Information Technology, the Maritime Safety Administration of the Ministry of Transport, and China Classification Society. Data is updated annually or via special revisions. Individual documents mostly use structured formats, including fields such as document number, issuing authority, effective date, applicable ship types, and technical parameter thresholds. Units for these fields primarily use shipbuilding-specific units such as gross tonnage, kilowatts, and meters.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The specialized units, multi-source hierarchical attributes, and long-document structure of shipbuilding policy data impose three constraints on multi-turn dialogue. First, specialized units require prompts to clearly specify matching industry terms such as gross tonnage and kilowatts, to avoid answer deviations caused by parameter confusion. Second, policies issued by multiple departments have different priority levels. Multi-turn dialogue must distinguish between issuing entities and effective dates in context memory, to ensure responses comply with the latest regulatory requirements. Third, individual policy documents are lengthy. The context window for multi-turn dialogue must accommodate long-text retrieval, to avoid truncation or omission of key policy clauses.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 characters | Adapts to the length of individual shipbuilding policy documents, to avoid truncation of key clauses |
| Question Optimization | Enabled | Automatically breaks down professional shipbuilding industry questions, and matches the context logic of multi-turn dialogue |
| Input Length Limit | 1000 characters | Intercepts overly long questions in advance, to avoid triggering errors without prompts |
chatId | Generate unique identifiers per session | Distinguishes context for different multi-turn sessions, to prevent confusion of session data |
messages | Splice complete context according to session history | Passes previous questions and policy reference information, to support multi-turn logical association |
variables | Pass industry variables such as ship type and effective date | Accurately matches policy clauses applicable to specific ship types, improving response accuracy |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Symptom: Multi-turn dialogue fails to retain broken-down sub-questions, and subsequent replies do not link back to the initial question. Cause: Did not splice complete session context as required by
messages, only passed the current single-turn question content. - Symptom: The dialogue request interface returns results without the cite reference ID field. Cause: Did not pass the metadata identifier of policy documents in
variables, and did not configure parameter logic for associated references. - Symptom: An error is thrown directly after input text exceeds the limit, with no intercept prompt. Cause: No input length verification rule was set, and no measures were taken to truncate overly long content in advance or prompt for question adjustment.
How to Confirm Configurations Are Properly Set
- Initiate a multi-turn question containing shipbuilding-specific terminology, and confirm that subsequent replies link back to the broken-down content of prior questions.
- Review interface return results to confirm the inclusion of the cite reference ID field.
- Input a question exceeding the preset length, and confirm the system intercepts the request in advance and provides an adjustment prompt.
- Confirm that the conversation record for a specified session can be deleted using the
chatIdidentifier.
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 2026-09-28.