What the data for this category looks like
Business data for refined oil sales originates from three system types: gas station POS terminals, member management systems, and invoicing ledgers. Data updates follow two rhythms. Single refueling, recharge, and invoicing transactions are synchronized in real time. Daily business summary data is archived in batches. Documents are stored in structured table format. Core fields include fuel grade (enumerated values such as 92#, 95#, 98#), fuel volume (unit: liters), transaction amount (unit: yuan), business process node, and member card number. Some fields use fixed enumerated types, with no free-text redundant fields.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-turn dialogue must accurately match fixed business nodes such as refueling, recharge, invoicing, and card application for refined oil sales structured enumerated business data. This avoids process confusion caused by generalized inquiries. Dialogue context must retain the most recent business processing status for real-time updated transaction data. This prevents repeated requests for already provided information such as member card numbers and fuel volume. Fields have clear attached units. Prompt engineering must enforce use of liters and yuan as units in outputs to avoid unit confusion. Enumerated business fields must preset optional values in the dialogue flow to reduce user input errors. The flow must also support real-time pulling of dynamic data such as latest fuel prices and gas station inventory. This ensures dialogue content aligns with current business rules.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generated by concatenating gas station ID, business type, and session timestamp, in the format station_{id}_{type}_{timestamp} | Matches scenarios with multiple stores and multiple business types for refined oil sales, ensuring dialogue context binds to the corresponding gas station and business |
messages | Retain the most recent 10 business interaction messages | Avoids overly long context interfering with current business process guidance, and adapts to lightweight dialogue for real-time transaction data |
variables | Inject current gas station's fuel grade, real-time price, and available business node list | Ensures dialogue prompts can call real-time business data, meeting requirements for structured business fields |
| Maximum context token count | 8000–12000 tokens | Covers multi-turn interactions for a complete single business process, adapting to multi-step processes such as refueling, recharge, and invoicing |
| Batch dialogue execution array | Insert tasks in the preset business order of refueling, recharge, and invoicing | Matches the linear business process flow of refined oil sales, ensuring multi-turn dialogue executes in sequence |
Three Common Misconfigurations
- Phenomenon: Unclosed reference mark garbled text appears at the end of dialogue output, and subsequent output automatically switches to normal quotation marks. Cause: The prompt does not clearly specify punctuation closure rules for dialogue content, and does not verify punctuation in multi-turn dialogue context.
- Phenomenon: After inserting multiple sets of AI dialogues into batch execution nodes, the business node order of some dialogues is disrupted. Cause: No
chatIdor business identifier is bound to each set of dialogues, leading to context confusion. - Phenomenon: Empty fuel price data returns when calling
variablesin dialogue. Cause: No real-time data pulling trigger rule is configured, causing variables to not synchronize updated latest gas station business data.
How to Verify Successful Configuration
- Initiate a multi-turn dialogue for a simulated refueling business, check that output uniformly uses liters and yuan as units, with no unit mixing.
- View dialogue logs, confirm the
chatIdfield includes identifiers such as gas station ID and business type, and no duplicatechatIdexists across different sessions. - Trigger batch dialogue execution, verify that the execution order of each set of AI dialogues matches the preset business process nodes.
- Check expiration rules for dialogue-associated files, confirm retention duration can be adjusted as needed to meet business data storage requirements.
The configuration values provided here 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.