What This Category of Data Looks Like
Civil aviation airport business process data is primarily sourced from flight display systems, check-in systems, security inspection scheduling systems, and baggage sorting systems. The update rhythm is near real-time: flight dynamic data is refreshed every 1 to 5 minutes, while business process data is updated monthly alongside airport operation adjustments. Data documents are categorized by flight number and business type (check-in, security inspection, boarding, baggage claim). Core fields include flight number, terminal number, counter/channel number, deadline, and precautions. Units include minutes, terminal numbers, counter numbers, and similar identifiers.
Constraints Imposed on Multi-Turn Dialogue and Prompt Engineering
Near real-time flight data requires that dialogue context be filtered for expired information promptly, to avoid pushing outdated gate or counter information to users. Subfields across multiple business types require prompts to extract the current business process step the user is handling via variables, reducing loading of invalid data. Users may supplement or change flight information during dialogue, so multi-turn dialogue must support dynamically updating passed variables to ensure each response matches the latest business data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the most recent 20 dialogue context entries | Civil aviation business process conversations typically focus on the current flight process; excessive historical context will interfere with the accuracy of the current prompt |
variables | Pass three core variables: flight number, terminal, business type | Civil aviation business is segmented by flight and terminal; variables enable precise matching of corresponding process data |
chatId | Bind the flight identifier the user is currently processing | The same user may handle multiple flights; chatId can isolate dialogue context for different flights |
| Context window limit | 800–1200 characters | Core fields of civil aviation process data have concentrated lengths; this range covers complete process steps |
| Timeout threshold | 60 seconds | Airport business processes require rapid responses; excessive timeout will negatively impact user experience |
Three Common Mistakes
- Phenomenon: Garbled quotation marks appear at the end of AI dialogue node output, followed by standard quotation marks in subsequent responses. Cause: Failure to specify output format specifications in the prompt, leading to abnormal punctuation handling for civil aviation business terminology.
- Phenomenon: The
chat:LLM_model_response_emptyerror is triggered, with no response content from the large model. Cause: Failure to limit the range of flight data passed viavariables, and failure to control token consumption of the prompt, exceeding the token limit for a single model call. - Phenomenon: Global variables fail to correctly pass the flight number entered by the user. Cause: Failure to correctly bind the parameter mapping for
variablesin the dialogue flow, leading to variables not being carried over during the dialogue.
How to Verify Correct Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Initiate a simulated dialogue, enter flight number, terminal, and business type information, and verify whether
variablesin the backend log correctly capture the corresponding parameters. - Trigger multi-turn dialogue interaction, check whether the
messagescontext only retains the most recent valid dialogue content, with no redundant historical interference. - Simulate invalid input (such as a non-existent flight number), check whether the model correctly prompts the user to supplement or correct the information.
- Simulate a temporary flight change scenario, check whether the dialogue can dynamically update
variablesand match the latest process guidance.
The 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.