What Data for This Category Looks Like
Data sources include internal enterprise security compliance documents, third-party vulnerability platform announcements, past marketing interaction records, and industry security standards. Update frequency: vulnerability-related data is synced in real time, compliance documents are updated quarterly, and marketing interaction data is aggregated daily. Document structure mixes structured fields and unstructured content. Structured fields include CVE ID, CVSS score, affected asset type, and risk level. Unstructured content includes repair guides and attack cases. The unit for CVSS score is 0-10, and risk level uses high/medium/low enumerated values.
What Constraints These Characteristics Impose on Multi-turn Dialogues and Prompt Engineering
The mixed structured and unstructured data characteristics require prompts to explicitly specify security-specific fields to be extracted, to avoid vague model outputs. Real-time updated vulnerability data requires multi-turn dialogues to connect to real-time recall interfaces, and static knowledge base caching should not be relied on. During multi-turn inquiries, users often first ask about the risk level of a specific vulnerability, then link to corresponding marketing content. Therefore, dialogue context must retain key information such as previous vulnerability IDs and risk levels to avoid repeated questions. The precise measurement requirement for CVSS scores requires clear verification rules for numerical ranges in prompts, to prevent the model from assigning incorrect values.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Cybersecurity documents often contain long sections of vulnerability repair guides and compliance clauses, requiring sufficient context to associate previous questions about vulnerability IDs and risk levels |
recallTopK | Top 6 entries | Security marketing content needs to match multiple associated dimensions: vulnerability type, asset type, risk level. Too many recalled entries will increase context redundancy, while too few will miss matching items |
similarityThreshold | 0.72–0.85 | Semantic similarity of security terminology is relatively high. A threshold that is too low will introduce irrelevant content, while a threshold that is too high will fail to match similar vulnerability types and marketing scenarios |
reasoning_effort | Medium | Requires verification of the accuracy of structured fields such as CVSS scores and vulnerability IDs. Medium computational power consumption balances reasoning accuracy and response speed |
pluginTimeout | 120 seconds | Real-time recall of vulnerability data requires waiting for third-party interface responses. An overly long timeout will cause dialogue interruptions, while an overly short timeout will fail to obtain complete security data |
systemPromptTemplate | Must include instructions such as "prioritize extracting fields including CVE ID, CVSS score, affected assets" and verify numerical rationality | Adapts to the structured characteristics of cybersecurity data, and prevents the model from generating content that does not comply with security specifications |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: Dialogue returns contain unprocessed
<think>tags. Cause: No filtering rules for model output format are configured, and model thinking content is not stripped. - Symptom: Configured guide prompts do not take effect, and the model does not extract security fields as required. Cause: The system prompt does not explicitly specify the priority of security-specific fields, leading to failed prompt parsing.
- Symptom: Real-time security data interface calls return 504 status codes. Cause: The
pluginTimeoutparameter is set too short, and insufficient time is reserved to obtain the latest data from third-party security platforms.
How to Verify Proper Configuration
- Initiate multi-turn inquiries containing multiple vulnerability IDs, and verify whether the marketing content returned by the model is associated with the previously mentioned vulnerability types and risk levels.
- Review the historical records of the dialogue context, and confirm that key security field information from the past 3 or more turns is retained.
- Trigger real-time security data recall, and check whether the returned interface fields include required items such as CVE ID and CVSS score.
- Test switching between different models, and confirm that there are no abnormal interruptions in recalled security data and marketing content.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.