This document is compatible with FastGPT v4.17.0.
What the data for this use case looks like
Typical processing stakeholders for this use case are after-sales engineers, project managers, and R&D support teams. Data sources primarily include customer-submitted warranty tickets, system operation logs, project contract warranty clauses, and software version change records. Daily processed documents include warranty tickets, defect confirmation sheets, repair records, and customer confirmation sheets. Core fields include defect ID, responsible party, repair deadline, and customer confirmation status. Data is updated daily or per ticket, synchronized with defect handling progress.
Constraints on multi-turn dialogue and prompt engineering
Data sources for this use case are scattered but highly correlated. Multi-turn dialogue is required to gradually align information across sources. For example, match error logs in system logs to the liability scope defined in contract warranty clauses. Fields in individual documents are relatively fixed but require precise extraction. Prompts must explicitly specify exclusive warranty-related fields. Data updates are processed per ticket, so context must be bound to the unique identifier of the current ticket to avoid mixing information across tickets. Liability boundary judgment is the most time-consuming step. Multi-turn dialogue must guide information sorting incrementally, ensuring each confirmation step is traceable to prevent subsequent warranty disputes.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Unique identifier bound to the current warranty ticket | Used to associate multi-turn dialogue context for the same ticket, avoiding cross-ticket confusion |
messages | Retain the most recent 20 turns of dialogue content | Matches the single-ticket processing rhythm of warranty tickets, preventing context overload |
variables | Inject the current ticket’s defect ID and contract warranty clause snippets | Provide category-specific field data for prompts |
| Maximum context length | 800–1200 characters | Adapts to the length of individual warranty-related documents, preventing exceeding model processing limits |
| Single-turn dialogue recall limit | Top 3 historical records | Focus on information related to the current defect, reducing interference from irrelevant content |
Three Common Misconfigurations
- Failing to pass
chatIdwhen calling the dialogue interface, preventing context association. This occurs when the unique identifier of the current warranty ticket is not bound, splitting multi-turn dialogue into separate sessions. - Incorrectly declaring supported file types in the configuration file, preventing reading of warranty ticket attachments. This happens when common document formats for this use case are not matched, and configuration items are omitted.
- Failing to adapt prompt language specifications to the target scenario, leading to misinterpretation of cross-language warranty clauses. This occurs when prompt language parameters are not adjusted per requirements, and corresponding language variables are not bound.
How to Verify Proper Configuration
- Initiate a new warranty defect dialogue, check if the
chatIdof the current ticket is automatically associated in the dialogue history. - Upload a warranty ticket attachment, confirm the system can correctly read and reference the attachment content in the dialogue.
- Switch the prompt language to the target language, verify that returned liability boundary judgment content uses corresponding terminology.
- Initiate more than 3 consecutive dialogue turns, check that the system retains context from previous defect descriptions and repair records.
The values provided here 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.