What the data for this category looks like
Engineering consulting supervision business data mainly comes from on-site supervision logs, side-by-side inspection records, sub-project acceptance reports, contract ledgers, rectification notices, and similar materials. Data updates follow project milestones. Daily inspection data is updated daily, while acceptance-related data is updated immediately after acceptance is completed. Each document contains structured fields and unstructured text. Structured fields include supervision unit, project number, acceptance location, rectification deadline, acceptance result, and other items. Units include square meters, working days, ten thousand yuan, and others. Unstructured text mostly consists of on-site problem descriptions and handling opinions.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The mixed structured and unstructured nature of supervision business data requires that multi-turn dialogue distinguish between different types of context information. Prompts must clearly specify the output format and unit requirements for structured fields. Data is updated according to project milestones and has a clear project cycle, so dialogue context must filter expired project data to avoid cross-project information confusion. The messages parameter for multi-turn dialogue must be bound to the exclusive fields of the current project to prevent irrelevant data from being included in the context scope.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Adapt to the mixed text length of supervision documents and avoid content truncation |
| Number of recalled conversation history entries | Top 3–5 turns | Reduce redundant context usage and focus on the latest communication content for the current project |
variables binding | Bind project ID and acceptance location fields | Limit dialogue to only use exclusive data for the current project |
| Output format verification | Mandatory inclusion of rectification deadline and acceptance result fields | Match the required output items for supervision business |
chatId binding | Bind the unique identifier of the current project | Isolate dialogue contexts for different projects and prevent data confusion |
| Context time filtering | Only recall project data from the last 30 days | Filter historical records of expired projects and ensure information timeliness |
Three common misconfigurations
- Symptom: When the model outputs a Markdown table, the content is truncated, and the interface displays
...[hide 38432 char. Cause: The maximum context length is not configured, resulting in insufficient dialogue window capacity to carry complete table content. - Symptom: The LLM reply content exceeds the scope of the knowledge base and includes unsent supervision data. Cause: The prompt does not clearly limit only using uploaded supervision logs and acceptance report data, and does not bind exclusive business fields through
variables. - Symptom: Cross-project supervision data appears in multi-turn dialogue. Cause: The
chatIdor project ID field is not bound, resulting in mixed recall of contexts from different projects.
How to confirm the configuration is correct
- Upload a single supervision acceptance report as test data, initiate a query that includes acceptance location and rectification requirements, and verify that the reply only uses the content of this test data.
- Initiate a multi-turn dialogue, ask about the supervision progress of different projects separately, and verify that the dialogue only recalls project data bound to the current
chatId. - Test the output of rectification notices in Markdown format, and verify that the table is fully displayed without truncation.
- Upload an audio test file, and verify that the supported audio formats of the system match the configuration requirements.
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.