What data does this category include?
Core data for this category originates from monitoring alerts, log platforms, APM tools, and user feedback. Updates occur in real time or immediately after a single incident is triggered. Daily generated documents fall into four categories: fault tickets, incident timelines, emergency response plans, and fault notifications. Each document type has fixed fields: fault tickets include alert time, affected services, and stop-loss operations; incident timelines record disposal nodes sorted by timestamp; emergency response plans bind recovery steps for corresponding services; fault notifications include impact scope and recovery duration. Primary users are SREs, on-call operations staff, and on-call development leads. Document sets for individual incidents are dynamically updated as disposal proceeds.
Constraints Imposed on Multi-turn Conversations and Prompt Engineering
Real-time data updates and dynamically changing document sets for single incidents require multi-turn conversations to bind to a unique identifier, preventing cross-incident context confusion. Documents have fixed field structures, so prompts must explicitly require output matching the corresponding document’s field format to avoid generating invalid content without matching fields. Multiple data sources must be synchronized during the disposal phase, so conversations must support injection of external real-time data to ensure generated content aligns with current incident status. Severe consequences can result from errors, so conversation outputs must clearly distinguish between context cache and reply content to enable subsequent auditing and traceback.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding switch | Enable and bind to the current incident ID | FastGPT v4.17.0 natively supports this parameter configuration, ensuring multi-turn conversations only associate with the current fault incident and prevent cross-incident context contamination |
messages field filtering | Retain only the three fields: alert time, affected services, stop-loss operations | Matches the fixed document structure of fault tickets and reduces generation of invalid content |
variables injection configuration | Inject the latest 5 monitoring alert data | Supplements real-time status of the current incident and improves accuracy of generated content |
| Context retention length | First 1000 characters | Controls context volume to avoid exceeding processing limits, calibrated based on actual testing |
| Conversation timeout setting | 600 seconds | Avoids long-term resource occupation, calibrated based on actual testing |
| Prompt template binding | Bind the dedicated template for incident response document drafting | Aligns with standardized output requirements for operations and maintenance disposal |
Three Common Misconfigurations
- Phenomenon: Content returned after calling the knowledge base is unrelated to the current fault issue, and instead answers fault issues for other services. Cause: The current incident context is not bound via
chatId, causing the conversation to call knowledge base data from other historical incidents. - Phenomenon: Conversation output returns both new context and AI replies, but the roles of the two cannot be distinguished. Cause: Output requirements for distinguishing context cache and reply content are not explicitly specified in the prompt, leading to user confusion.
- Phenomenon: Global variables configured in the workflow are not correctly injected into the prompt. Cause: The names of global variables to be injected are not declared in the
variablesparameter, causing the variables to fail recognition.
How to Verify Correct Configuration
- Manually trigger a fault simulation conversation, check that the conversation only associates with the current incident ID, with no cross-incident data mixed in.
- Generate a draft fault ticket, check that the output content includes the preset fixed fields and complies with document structure requirements.
- Inject a test monitoring data entry, check that the generated content includes the test data to confirm the
variablesparameter takes effect. - View conversation logs, confirm that the
messagesfield only contains conversation content for the current incident, with no redundant historical data.
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 2026-10-19.